Sunday: Mostly sunny. Highs in the 60s at the coast, 70s inland.
Monday: Clouds increase. Highs around 70.
High pressure building over New England results in a bit of a split decision Sunday. Near the shore from Cape Cod through Boston and into Portland, Maine, the wind is coming in off of the ocean at 10 to 15 mph with temperatures holding in the 60s, or even falling back into the 50s late in the day. We also may have a fog bank move in before sunset.
At the same time in Western New England, where a few spots started off in the 30s, we are warming into the 70s with a good deal of sunshine, mixed with mid and high-level clouds.
Overall it is a nice Sunday in New England. In Foxborough where the Patriots are set to play a game versus the Atlanta Falcons at 8:30 p.m., the temperature will be falling through the 50s, with near calm air.
As high pressure moves off to the east Monday, the return flow from the south and southeast will bring humidity up a bit, along with a little bit more in the way of cloudiness, but it still will not be too bad. Any early fog should burn off, with a high temperature of 70 degrees.
We are tracking a storm system that caused five tornadoes in Oklahoma on Saturday. This slow-moving front arrives in New England during the day Tuesday with thicker clouds and showers in western New England. Highs Tuesday will again be near 70 degrees, with the noticeably more humid air.
The slow-moving front gets to New England on Wednesday, with periods of rain heavy at times and thunderstorms. Wind from the south may gust past 45 mph especially in the hills and at the shore. High tide late in the day may have some coastal erosion but astronomical tides are not running that strong this week.
Rainfall totals may exceed three inches in spots. A drought denting rain seems to be in store, with perhaps even some localized flooding during the day Wednesday.
The front edges into Maine on Thursday with cooler and drier air for Western New England. It will stay wet in Maine, but most of us will see brighter weather and cooler air, with high temperature in the 60s east to 50s west.
Though an upper-level low is still nearby, it looks like we have brighter weather for Friday and Saturday with sun and clouds mixed, and temperatures more seasonable around 60 degrees.
Another energized system will move in later in the weekend, with a chance for more showers and cooler air as we head toward Halloween.


text:
! This program tests table-reading and writing
program read_table_test

  implicit none

  double precision, target, dimension(:,:,:), allocatable :: rate_table_temp
  double precision, target, dimension(:,:,:), allocatable :: rate_table_1
  character(len=*), parameter :: rate_table_file_1 = '23Ne-23Na_eemission.dat'
  character(len=*), parameter :: rate_table_test_1 = '23Ne-23Na_eemission.out.min'
  integer, parameter :: num_header = 7
  integer, parameter :: num_rhoy = 152
  integer, parameter :: num_temp = 39
  integer, parameter :: num_vars = 6
  integer :: i, j, k

  allocate( rate_table_temp(num_temp, num_rhoy, num_vars+2) )

  open(unit=11, file=rate_table_file_1)
  do i = 1, num_header
     read(11,*)
  end do
  do j = 1, num_rhoy
     do i = 1, num_temp
        read(11,*) ( rate_table_temp(i, j, k), k=1, num_vars+2 )
     end do
     if (j/=num_rhoy) then
        read(11,*)
     end if
  end do
  close(11)

  allocate ( rate_table_1(num_temp, num_rhoy, num_vars) )
  rate_table_1(:,:,:) = rate_table_temp(:,:,3:num_vars+2)

  open(unit=12, file=rate_table_test_1)
  do j = 1, num_rhoy
     do i = 1, num_temp
        write(12, fmt='(8 (ES25.14))') ( rate_table_1(i, j, k), k=1, num_vars )
     end do
  end do
  close(12)

end program read_table_test

redpajama_set_name: RedPajamaGithub


Is there a way or service that will allow me to access and edit my geodatabase back in the office from a mobile device on the field?
Public access wouldn't be required, but VPN access might, or just a secured web site -- call it "connectivity" with the AGS host's service. Any way you roll it, the IT folks should be in on the conversation.
Sure is it called ArcGIS Server and FeatureServices or publish your FeatureClass to your AGOL account as a FeatureService.
Thanks Robert for your reply but i'm still having some issues.
I created the feature service and I'm able to access it through ArcMap , edit, and then the changes are reflected in the local SDE Geodatabase. This however only works when both machines are on the same network. When I try with one of the machines being on a different network I get this error message: " Cannot connect to this server"
That's what I'm aiming for, to be able to edit from the field which obiously would be over different internet networks or providers.
This would require you (your IT people) to provide access to your ArcGIS server to the web/public. You would be able to use a public url to access the server like esri sample servers.
Yep, I probably should not have used the word "public". Maybe "external" would have been a better choice.


text:
let webpack = require('webpack');
let path = require('path');
let CleanWebpackPlugin = require('clean-webpack-plugin');
let LodashModuleReplacementPlugin = require('lodash-webpack-plugin');
let ExtractTextPlugin = require('extract-text-webpack-plugin');
const extractSass = new ExtractTextPlugin("./css/style.css");
const extractKuma = new ExtractTextPlugin("./css/kuma.css");
const extractCSS = new ExtractTextPlugin("./css/styles.css");

let TransferWebpackPlugin = require('transfer-webpack-plugin');
let staticFilesPath = path.resolve(__dirname, "src/main/resources/static");
let buildPath = staticFilesPath + "/build";
let nodeModulesPath = path.resolve(__dirname, 'node_modules');
module.exports = {
    entry: {
        main: staticFilesPath + '/main.js',
        login: staticFilesPath + '/login.js'
    },
    output: {
        path: buildPath,
        filename: "js/[name].js",
        chunkFilename: "js/[name].js"
    },
    // 此项配置可以将某些库设置为外部引用,内部不会打包合并进去。
    externals: {
        'react': 'React',
        'react-dom': 'ReactDOM',
        // 'lodash': '_',
        'babel-polyfill': 'window',
    },
    debug: true,
    devtool: 'source-map',
    plugins: [
        new CleanWebpackPlugin(['build'], {
            root: staticFilesPath,
            verbose: true,
            dry: false,
        }),
        new webpack.optimize.CommonsChunkPlugin("common.js", ["main", "login"]),
        new webpack.ProvidePlugin({
            $: "jquery",
            jQuery: "jquery",
            "window.jQuery": "jquery"
        }),
        extractCSS,
        extractKuma,
        extractSass,
        new LodashModuleReplacementPlugin,
        // 把指定文件夹下的文件复制到指定的目录
        new TransferWebpackPlugin([
            {from: nodeModulesPath + '/react/dist/', to: "../lib/react"},
            {from: nodeModulesPath + '/react-dom/dist/', to: "../lib/react"},
            {from: nodeModulesPath + '/es5-shim/', to: "../lib/es5-shim"}
        ])
    ],
    module: {
        loaders: [
            {
                test: /\.js$/,
                exclude: [nodeModulesPath],
                loaders: ['babel-loader'],
            },
            {
                test: /\.(less)$/,
                loader: extractKuma.extract("style-loader", "css-loader!less-loader")
            },
            {
                test: /\.(scss)$/,
                loader: extractSass.extract("style-loader", "css-loader!sass-loader")
            },
            {
                test: /\.(css)$/,
                loader: extractCSS.extract("style-loader", "css-loader")
            },
        ],
        postLoaders: [
            {
                test: /\.js$/,
                loaders: ['es3ify-loader'],
            },
        ],
    }
};


A 100m Youth Olympics Gold medalist, Rosemary Chukwuma says her focus after her achievement in the Youth Olympics is to participate in the 2020 Olympics in Tokyo.
Chukwuma, told the News Agency of Nigeria (NAN) in Lagos on Monday that she hoped to participate in the 2020 Olympics and to also win a medal for the country.
She promised not to disappoint her fans and the country as a whole in 2020 Olympics.
"My next target is to participate in the Tokyo 2020 Olympics, which I know will be made possible by God and hard work.
"If I make the team for the Summer Olympics, I strongly believe that I will try my best to win a medal, even if it is Bronze, Silver or Gold.
"I strongly believe that my God will do it for me because what I achieved in Argentina wasn't by my power but through the strength given to me,'' she said.
Concerning her journey as an athlete, she said it had been stressful but she was grateful to God for her achievements.
"The journey hasn't been easy. It has been very stressful but with the help of God, I am improving,'' Chukwuma said.
She, however, thanked the management of `Kid 'n Play' for discovering her and her coach.
"I say a very big thank you to `Kid 'n Play' for discovering me and my coach, Murphy Adaji.
"I will always make them proud,'' Chukwuma said.
The News Agency of Nigeria reports that Chukwuma anchored the women's 4x100m team to Commonwealth Bronze and African Championships Gold medals respectively.
She also won a bronze medal in the 100m and a silver medal in the 200m at the 2018 ECOWAS U20 Games in Ghana.


A new study asked oncology nurses for their opinions on the best future direction of cancer research.
According to patients, palliative and end-of-life care should be prioritized for future study, with a focus on care in the community and access to specialist palliative care and oncology support. Patients also expressed the need for further research on cognitive changes that might occur as such changes can be distressing to both the patient and their families.
In contrast, oncology nurses prioritized the importance of future research on eHealth and technology to manage cancer symptoms at home. This suggests that nurses are responsive to possible service changes.
Both groups prioritized further research into identifying early cancer symptoms and the need for psychosocial support and anxiety management services. Identification of early cancer symptoms is consistent with previous findings that early cancer diagnosis leads to more effective care and improved survival rates.


text:
Reggie's Corner England vs Ireland 2016 Special
26 Feb Reggie's Corner England vs Ireland 2016 Special
It doesn't get much better than this. It's one of the greatest sporting rivalries in the rugby calendar and like in any sport where we play England, the one we all want to win.
Twickenham is a brilliant experience for players and supporters alike. The atmosphere builds from early in the day and with the slightly later kick off of 4:50 there will be some fairly merry English and Irish fans trying to out sing each other outside the stadium from mid-day onwards. Sweet Chariot vs Fields of Athenry being the precursor to the main event.
The fact that both rival fans, can and do mix with such ease before a contest like this, with a minimal police presence watching on and enjoying the entertainment, is somewhat unique and a huge credit to the fans who enjoy this great game.
For the players however once that first whistle blows all friendships go out the window. The English are good and at home they are better. An English rugby player is a dangerous animal and in his own den almost impossible to overcome.
I remember playing them in 2004 in their first game at home since they won the 2003 World Cup and I'm not sure I can recall a more intimidating game in which to be involved. The fear of being smashed, shamed and embarrassed by this incredible team was definitely my overriding emotion and to be honest probably the reason we did as well as we did. We wrecked the home coming party, beat the English in their home, partied till the early morning and went on to win our first Triple Crown for nineteen years. Fear can do funny things to a man!
That fear will be no different this weekend for the current Irish team. Plagued with injury and trying out new and unknown combinations will have them wondering about their ability to overcome an English side who are on a bit of an upward curve.
I'm not convinced about this English side yet under Eddie Jones. There is no doubt he is trying to get them to adopt a different style of play and to have increased confidence in their game but I'm not sure they have been really tested yet.
The question is can this Irish side test them. There is no doubt Joe Schmidt will have a few tricks up his sleeve but it's whether or not this new and somewhat exciting team can implement them. I delighted to see McCloskey and Van Der Flier in the team. They are two inform players who offer a real threat but this is a big step up for them.
Ireland have got to try to play with ambition in this test. If they stick to type and don't try a more expansive offloading game they will be beaten. Opposition teams place a huge amount of trust in analysis and Ireland are currently too easy to analyze.
We have to bring a new dimension to this match and try to catch the English out. Unpredictability has to be our weapon. We have gone with a new look team albeit forced to do so, but it's time to take some risks and throw caution to the wind.
I for one will not criticize a team that tries to be different and offer threats. It will be hard to defend them though if they try to hammer home the same old game plan to no avail against a quality side like England.
My head says England but my heart says Ireland.
Enjoy the game

redpajama_set_name: RedPajamaCommonCrawl


text:
Novak Djokovic 'disappointed' after losing visa appeal - what he said about decision
Symptoms of Multiple Sclerosis and if the disease is linked to glandular fever explained
Irish League Football
Your NI
This is how to claim for PPI and the information you'll need before tonight's deadline
Updated Thursday, 29th August 2019, 10:55 am
Have you ever had a mortgage, credit card or loan? If so, you may have been mis-sold PPI (Photo: Shutterstock)
Have you ever had a mortgage, credit card or loan? If so, you may have been mis-sold PPI.
There's a chance you were sold PPI without even knowing - in some cases salespeople didn't explain these policies when they were sold.
If you've not checked for PPI, you might be missing out on a lot of money - but time is running out to make a claim.
What is PPI?
Payment Protection Insurance (PPI) was added to finance products including credit cards, store cards, personal loans, car loans and mortgages between 1980 and 2010.
It was intended to protect people if they were struggling to keep up with their payments due to illness or unemployment, but it was widely mis-sold.
For some, PPI may have been unsuitable for them, with others have been pressured into buying it or unaware that they had it.
Some people were sold policies that would never end up paying out due to them being self-employed or retired, while some were told it was compulsory.
After this wide mis-selling of PPI was discovered, lenders set aside £45 billion to pay out in compensation - with around £35 billion having been paid out to claimants so far.
The average customer payout for loan PPI compensation is around £2,000, according to The Money Advice Service.
How do I claim?
The Money Advice Service suggests that you should firstly, find all the relevant documents you have and make copies, including anything that shows you've taken out a policy and shows you making payments for it.
If you're not sure whether a document is relevant, you should still copy it.
You can use the Money Advice Service PPI complaints template letter to write a letter to the mortgage, loan or credit card provider who sold you the PPI or you can fill in the questionnaire which most of the banks have on their own websites.
This standard form is also used by the Financial Ombudsman Service.
Send all the documents you think might be relevant and explain why you think you were mis-sold PPI.
The Money Advice Service states: "If you don't get a response or decision within eight weeks, send a complaint to the Financial Ombudsman Service (FOS).
"The Ombudsman will ask you to fill out a questionnaire to decide whether or not you've been mis-sold PPI."
You can also claim via the Money Saving Expert free PPI reclaiming tool.
The very minimum you need to know is who the lender was, but if you're not sure of that, then you may be able to find this on your credit file.
You will also need to give your name (including the name used to take out loan/credit if different), current address and date of birth.
If you do have more information, such as details of the policy name, your address when you took it out, why you had it and why you think you were mis-sold, this may help to speed up the claim later on.
Martin Lewis said: "If you're not sure, just fill in the details anyway and check if you had PPI.
"I'm not encouraging you to make a spurious claim, more to just get in under the line and buy yourself more time to find out if you are due money later. If not, simply don't proceed any further later."
When is the deadline?
According to moneysavingexpert.com, the very last moment for submitting claims online with all major banks is 11.59pm on Thursday 29 August.
However, if you're going to do it by phone, the cut-off varies between 6pm and 11.59pm on the same day.
For postal submissions, some banks explain that it's alright for you to post the letter on 29 August, with others requiring that they must have received it by then.
Any claims in person will need to be done within bank opening hours.
However, some banks may treat you checking if you have PPI as starting a complaint with regards to the deadline cutoff.
Moneysavingexpert.com said: "Banks used to treat a 'check whether you had PPI' and 'complaint about PPI' as separate things.
"Now most banks have agreed with regulator the Financial Conduct Authority (FCA) that someone simply enquiring if they had PPI counts as starting a complaint for the purposes of the deadline."
This article was originally published on our sister site, Lancashire Evening Post.
Your Northern Ireland
More from Northern Ireland World
Antrim and Newtownabbey
Armagh Banbridge and Craigavon
Causeway Coast and Glens
Londonderry Derry City and Strabane
Lisburn and Castlereagh
Mid and East Antrim
Mid Ulster
Fermanagh and Omagh
Newry Mourne and Down
Ards and North Down


I've written in this space before about my mom's habit of "clipping." When she comes across an article she thinks I will find interesting, she "clips" it out of the newspaper or magazine and sends it to me.
Many months ago, she gave me a clipping from an article in the Wall Street Journal about President George W. Bush that said he liked haiku.
This isn't the first time my mom has clipped a Buckles comic strip for me. After all, in my family, we're devoted dog lovers and Buckles is all about the life of a spoiled dog.
And actually, my mom has clipped Buckles haiku for me before. It seems David Gilbert, the comic strip's creator, has a hidden love of haiku.
I've tried to contact Mr. Gilbert in the past to find out if he has a collection of Buckles haiku, or if he would be up for telling me more about his haiku streak. Maybe with this blog posting, I'll finally get a response!


text:
\section{Introduction}
\label{sec:intro}
\vspace{-0.1in}
Everyday, we are exposed to various images and video thanks to Facebook, Flickr, Youtube, Instagram and others. The content in these websites or applications are provided by the users. While uploading the multimedia content, files need to satisfy basic constraints such as format, size and resolution. However, these social media platforms do not assess the quality of multimedia content. Subjective quality evaluation is by far the best way to asses the quality of multimedia. However, it requires extensive amount of time and labor. Therefore, objective quality metrics are used to estimate subjective quality.

Most of the quality assessment methods estimate  the quality by calculating the degradations in the images and videos. Fidelity-based metrics calculate the accuracy of the processed content with respect to the original content whereas structural metrics model the perceived quality of visual data by considering the Human Visual System (HVS). However, fidelity and structure-based metrics are not sufficient to estimate the quality of experience for the end user. We claim in our work that we also need to consider the aesthetics within images and videos. Hence, in our work, we develop image quality measures that incorporate aesthetics as well as other structure-based and statistical models.

The authors in~\cite{Datta2006} proposed a computational approach to study aesthetics in photographic images. They studied aesthetics as a machine learning problem by extracting low-level features based on rules of thumbs in photography, common intuition and rating patterns. In~\cite{Hasler2003}, authors designed a colorfulness index to asses the quality rather than fidelity. Colorfulness index quantifies the colorfulness in natural images to estimate perceived quality by using the color distribution in the CIELab color space. In addition to the colorfulness feature, brightness, contrast, saturation and saliency were also used to asses the beauty rating of videos in \cite{Yildirim2013}. In \cite{Dhar2011}, the authors used composition, content and sky-illumination as high level attributes to predict aesthetics and interestingness.

Instead of focusing on the entire image, the authors in \cite{Luo2008} extracted subject regions using blind motion deblurring \cite{Levin2006} to detect areas that draw the most attention of HVS. The authors in \cite{Tang2013} used hue and scene composition features as global features whereas dark channel, face region and complexity as regional features. Sharpness, colorfulness, luminance, color harmony and blockiness were used in \cite{Moorthy2010} as low-level features to model visual aesthetic appeal. Composition-specific features such as relative foreground position and visual weight ratio were used in \cite{Bhattacharya2010} to asses photo quality and perform semi-automatic enhancement based on visual aesthetics. Instead of hand-crafting features that highly correlate with photographic practices and techniques, the authors in \cite{Marchesotti2011} used generic image descriptors such as GIST \cite{Oliva2001} and BOV (\cite{Csurka2004}, \cite{Sivic2003}) to asses aesthetics of images.

It is not straightforward to describe aesthetics. Therefore, we need to focus on the judgement of subjects. The authors in \cite{Luo2008} generated a video database by selecting videos from YouTube. The database contains 4000 high quality professional movie clips and 4000 low quality amateurish clips. The quality of the videos was assessed per frame basis and the average was computed to asses the quality of videos. The authors also used MSN Live Search to search for images and volunteers ranked the images on a scale between 1 and 5. A photography database with peer-rated aesthetics scores ranging from 1 to 7 was provided in \cite{Photonet}. Similarly, \cite{DPChallange} provided a large photo database with peer ratings based on quality ranging from 1 to 10. Authors in \cite{Datta08} randomly collected large samples from  \cite{Alipr}, \cite{DPChallange} , \cite{Photonet} and \cite{Terragalleria} that were annotated with aesthetics, quality, liking and emotion scores. Around 1 million images crawled by Flickr with textual tags, aesthetics annotations, and EXIF meta-data were provided in \cite{CLEF2010}. A large set of standardized, emotionally-evocative color photographs were provided with a wide range of semantic categories in \cite{IAPS}. Authors in \cite{Murray2012} generated a large scale database with score distributions, semantic and style labels and rich annotations including aesthetics.

In this work, we focus on the binary classification of the images based on aesthetics quality. We examine the descriptors used in the aesthetics image quality  literature  as well as  other commonly used image descriptors.  In section \ref{sec:literature}, we introduce state of the art computational aesthetics descriptors. Geometric descriptors are discussed in section \ref{sec:genericgeometric} and color descriptors are explained in \ref{sec:genericcolor}. We examine hybrid descriptors in section \ref{sec:generichybrid} and visual dictionary approach in section \ref{sec:genericvisual}. We compare the descriptors in section \ref{sec:comparison} and conclude our discussion in section \ref{sec:conclusion}.

\vspace{-0.1in}

\section{Aesthetics Descriptors }
\label{sec:mainbody}
\vspace{-0.1in}
  We can measure the success of the computational models  by how good they can estimate the average subjective scores. From the image sets referred in section \ref{sec:intro}, we use the \texttt{CUHK} database collected by the authors in \cite{Ke2006}. The images in the database are obtained from the photo contest website DPChallenge~\cite{DPChallange} along with the user ratings. From the obtained 60,000 images, the top 10\% images are selected as \texttt{good} and the bottom 10\% images are selected as \texttt{bad} images. Half of the image set is randomly selected to be used for training with labels and the other half is used for the classification tests. In the following sections, we briefly introduce the descriptors and examine their classification performances.

\subsection{State of the art descriptors }
\label{sec:literature}
Ke at al. \cite{Ke2006} designed aesthetics features using spatial distribution of edges, color distribution, hue count, blur, contrast and brightness. Datta et al. \cite{Datta2006}  used exposure of light, colorfulness, saturation, hue, rule of thirds, familiarity measure, wavelet based texture feature, size and aspect ratio, region composition, depth of field and shape convexity to asses image aesthetics. Tong et al. \cite{Tong2004} implemented a black-box approach by generating a set of low-level features and fusing these features using learning algorithms. In addition to extracting global features, Luo and Tang \cite{Luo2008} extracted subject regions to obtain local features of foreground and background. They calculated clarity contrast, lighting, simplicity, composition geometry and color harmony to model the aesthetics. Marchesotti et al. \cite{Marchesotti2011} used generic features instead of hand-crafted features to perform aesthetics-based classification. SIFT and color features were extracted and a global model was generated using Gaussian Mixture Model (GMM). Bag of Words (BoW) and Fisher Vector (FV) were used to obtain the statistics of feature distributions to train a Support Vector Machine (SVM) classifier.

\begin{figure}[tbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/literature_v2.pdf}
\vspace{-0.15in}
\caption{Classification accuracy of state of the art methods}
\vspace{-0.2in}
\label{fig:Literature}
\end{figure}

We summarize the classification accuracy of state of the art methods in Table~\ref{fig:Literature}. Hand-crafted global features can reach to a classification accuracy up to $76\%$ with Ke et al. \cite{Ke2006}. Local features proposed by Luo and Tang \cite{Luo2008} leads to an accuracy up to $93\%$. Generic features used by Marchesotti et al. \cite{Marchesotti2011} result in an  accuracy between $81.4\%$ and $89.9\%$. In the rest of the simulations, we use L1 soft-margin SVM classifier. We experimented other classifiers and observed that classification accuracy did not change significantly when we had a large set of training and test images.

\vspace{-0.10in}

\subsection{Geometric Descriptors }
\label{sec:genericgeometric}
GIST is a holistic representation of an image to model the shape of the scene \cite{Oliva2001}. Scene representation is classified with respect to naturalness, openness, roughness, expansion and ruggerdness. GIST uses the local and global energy spectrum to quantify the introduced metrics in the scene representation. We use three different configurations of the GIST features by varying the number of orientations per scale and number of blocks. GIST(16) and GIST(32) correspond to the configuration where we use an unlocalized energy spectra by setting the number of block to \texttt{1}. GIST(512) corresponds to the case where the number of blocks is set to \texttt{4}. Orientations per scales are all set to \texttt{8} for GIST(32) and GIST(512) where orientation scales are set to \texttt{4} for GIST(16). SIFT divides the image into \texttt{4x4} grids of cells and calculates histogram of image gradient directions as explained in \cite{Lowe2004}. GMM approximates the distributions by weighted sum of Gaussian models. In addition to being used as a feature, it is also used to generate the visual dictionary in section \ref{sec:genericvisual}. Maximally stable extremal regions (MSER) thresholds the image in the intensity channel. Threshold is swept from black to white to detect the connected areas that are unchanged over a large set of thresholds \cite{MSER2002}. Difference of Gaussians (DOG) convolves the original image with Gaussian kernels and subtracts the blurred images from each other. Difference of blurred images contains band-pass details that are used as image descriptors. We also detect corners and blobs to represent images using Hessian, Harris and Laplace operators. Implementation of most of the geometric descriptors are provided with VLFeat package which is an open and portable library of computer vision algorithms \cite{Vlfeat2008}.

\begin{figure}[tbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/geometric_v2.pdf}
\vspace{-0.1in}
\caption{Classification accuracy of Geometric Descriptors}
\vspace{-0.2in}
\label{fig:Geometric}

\end{figure}

Classification results for geometric descriptors are given in Figure \ref{fig:Geometric}. In here, we calculate the geometric descriptors by computing the average of each feature dimension over the whole image. We obtain the most accurate classification with $67.7\%$ in DOG, $67.2\%$ in SIFT and $67.1\%$ in HessianLaplace.
Generic geometric descriptors inherently contain information related to the spatial complexity and composition. Some of the geometric descriptors perform better than the others but none of them is sufficient for aesthetics classification since they do not focus on the basic dimensions of the aesthetics.

\vspace{-0.10in}

\subsection{Color Descriptors}
\label{sec:genericcolor}

Color descriptors are designed according to four main constraints: photometric robustness, geometric robustness, photometric stability and generality \cite{Khan2013}. In order to satisfy these constraints, color descriptors should be invariant to shadow, shading, light source configuration, view point, orientation and image quality. However, it is not possible to design descriptors that can satisfy all the constrains. Since image aesthetics is also influenced by these factors, we can use color descriptors as aesthetics metrics.

Color naming is introduced in \cite{ColorNaming2009} as an image descriptor, which calculates the color distributions similar to the bag of words approach. Relative locations of pixels do not effect the distribution since this approach only focuses on the color distribution of the pixels in the region of interest. It is originally used to assign linguistic color labels to image pixels and the main objective is to predict the color category that humans would perceive given a color measurement. In practice, color naming descriptor is a 11-D vector where each dimension corresponds to the distribution of main colors that can be sorted as follows: black, blue, brown, grey, green, orange, pink, purple, red, white and yellow. We also use the color naming method described in \cite{ParametricColorNaming2008}. Authors use a fuzzy $k$-means algorithm to obtain color labels from Munsell book of color. Color descriptor consists of member functions which map color elements to $[0,1]$ interval and the abbreviation JOSA is used to represent this descriptor in the results .

In addition to the distribution of colors, we can also consider the relative locations of the color pixels. We use discriminative color descriptor introduced in \cite{Khan2013} to cluster the color pixels in compact representations. Color pixels are clustered by maximizing the discriminative power using an information theocratical approach. We also use the color descriptors in the opponent color space as described in \cite{Weijer06}. Color descriptors are designed in a similar way to SIFT by calculating the histogram of gradients in hue. The hue color descriptor performs poorly when the saturation is low. In case of low saturation, we can use opponent color angle \cite{Weijer06}.

We use the default version of the color descriptors defined in \cite{ColorCodes}. Color descriptor matrix is composed of three rows where each row corresponds to the metric calculated over three constant regions. In addition, we take the average of the metric over three regions and use it as an additional descriptor which is shown with a suffix $(1x11)$. In case of discriminative color, we also calculate the color descriptors for a color dictionary size of $25$ and $50$.

\vspace{-0.1in}

\begin{figure}[htbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/color_v1.pdf}
\vspace{-0.2in}
\caption{Classification accuracy of Color Descriptors}
\label{fig:Color}

\end{figure}

Color naming (1x11) results in the highest classification accuracy with $73.6\%$ followed by JOSA(1x11) with $72.1\%$. When we compare with the generic descriptors, color descriptors perform better. In the literature, variations of color distribution and harmony are commonly used as hand-crafted features to model aesthetics as it is mentioned in section \ref{fig:Literature}.

\vspace{-0.10in}

\subsection{Hybrid Descriptors}
\label{sec:generichybrid}
Color, geometric and aesthetics descriptors analyze the images in different aspects and they can be complementary to each other for classification. However, they can also contradict with each other in terms of classification decisions. We combine some of the descriptors defined in the previous parts to obtain a hybrid descriptor. Classification accuracies of the hybrid descriptors are shown in Figure \ref{fig:Hybrid}.

\begin{figure}[htbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/hybrid_v3.pdf}
\vspace{-0.1in}
\caption{Classification accuracy of Hybrid Descriptors}
\label{fig:Hybrid}
\end{figure}

\begin{figure*}[t]
\vspace{-0.1in}
\centering
\includegraphics[width=0.80\linewidth]{Figs/training_v3.pdf}
\vspace{-0.3in}
\caption{Training pipeline}
\label{fig:Training}
\vspace{-0.2in}
\end{figure*}

We use Color naming (1x11) as the color descriptor which is abbreviated as CN and SIFT is used as the geometric descriptor. When CN and SIFT are combined together, classification accuracy gets lower than the accuracy of CN but higher than the accuracy of SIFT with $70.1\%$. Optimal scale (OS) is a feature introduced to quantify the amount of low frequency content in the image. Classification ratio increases by $1\%$ when OS is added to the descriptor. We add exposure of light, hue, rule of thirds, wavelet-based texture and size and aspect ratio features introduced by Datta et al. \cite{Datta2006} to our descriptor. Classification accuracy increases up to $75.7\%$. Finally, we add   brightness, saturation, average color and colorfulness features proposed by Susstrunk et al. \cite{Hasler2003} and \cite{Yildirim2013}. Our hybrid descriptor has a classification accuracy of $75.9\%$. Instead of training with the whole set, we train with the first $100$ good and bad images to experiment the effect of training set size. Classification accuracy drops from $75.9\%$ to $70.2\%$.

\vspace{-0.18in}

\subsection{Visual Dictionary Approach}
\vspace{-0.05in}

\label{sec:genericvisual}

In the previous sections, we extract features and combine them in a vector to form a descriptor. However, we do not use the distribution of the features. In contrast, in this section, we generate a visual dictionary to consider the distributional statistics of the features. The training pipeline including visual dictionary generation is shown in Figure \ref{fig:Training}.

We extract features from two different classes of images and these features are fed to a universal feature model to obtain a visual dictionary. Training features and universal model parameters are fed to a descriptor extraction module to obtain class I and class II descriptors. We train the classifier with the labeled descriptors to obtain a classifier model. We use SIFT, DOG, Color naming (1x11) and JOSA because they have the highest classification accuracy based on our experiments from previous sections. In addition to the default SIFT, we perform singular value decomposition to keep the first $N$ eigenvalues and remove the rest to reconstruct the feature vector. We experimented with different $N$ values and $30$ produced the highest classification accuracy. Gaussian Mixture model (GMM) is used as the universal feature model and Fisher Vector is preferred as the descriptor. In our simulations, we vary the number of Gaussians in the GMM and the training set size. For each feature, we perform simulations at least for $14$ different configurations and up to $22$. Configurations that lead to highest classification accuracy are given in Table \ref{tab:1}.

\begin{table}[htb]
\vspace{-0.10in}
\centering
\caption{Classification accuracy for the dictionary approach  \label{tab:1}}

 \begin{tabular}{|c|c|c|c|} \hline
\textbf{Features}&  \textbf{Number of} & \textbf{Training} & \textbf{Classification } \\
\textbf{Types}&  \textbf{Gaussians} & \textbf{Set Size} & \textbf{Accuracy (\%)} \\\hline
SIFT(SVD) &  \texttt{200} & \texttt{100} & \texttt{75.5}\\\hline
SIFT &  \texttt{200} & \texttt{100} & \texttt{74.0}\\\hline
CN&  \texttt{5} & \texttt{6000} & \texttt{72.6}\\\hline
DoG&  \texttt{2} & \texttt{6000} & \texttt{69.7}\\\hline
JOSA&  \texttt{5} & \texttt{6000} &  \texttt{67.6}\\\hline
\end{tabular}
\vspace{-0.20in}
\end{table}

\vspace{-0.15in}
\subsection{Descriptor Comparison}
\vspace{-0.10in}

\label{sec:comparison}

We observe that state of the art descriptors using local information leads to classification ratios up to $90\%$. Luo and Tong \cite{Luo2008} used hand-crafted features and Marchesotti et al. \cite{Marchesotti2011} used generic features to obtain high classification accuracy. As it was claimed in \cite{Marchesotti2011}, generic features can perform as good as hand-crafted features. However, examining local features in addition to global features have a more significant effect on the classification results than feature selection. The maximum classification ratio we obtain using geometric descriptors is $67.7\%$ and it is $73.6\%$ using color descriptors. Hybrid descriptors lead to $75.9\%$ and the dictionary approach leads to $75.5\%$ at most. Basic generic features such as color and geometric can perform as well as the state of the art global computational approaches. However, they do not perform as well as the ones that take spatial characteristics into account by using subject region extraction or spatial pyramid. The main disadvantage of the regional methods comes from the time and memory complexity. Subject region extraction used in \cite{Luo2008} is an exhaustive approach that requires significant amount of computational time and the spatial pyramid originally introduced in \cite{SpatialPyramid2006} requires significant amount of memory (Approximately 250GB of memory is required to store the extracted features of $1$ dataset out of $4$ in the CUHK dataset ).

\vspace{-0.1in}

\section{Conclusion }
\label{sec:conclusion}
\vspace{-0.1in}

In this paper, we compare generic, hand-crafted and aesthetics descriptors to examine the classification performance of image aesthetics. We have shown that basic generic descriptors can perform as well as the state of the art hand-crafted global descriptors. However, both generic and hand-crafted features are limited in terms of classification when we do not consider the spatial distribution of features. Spatial pyramid and subject region extraction are the main factors that lead to high classification accuracies in the literature. But they require significant amount of computational time and memory. In our future work, we will examine the correlation between the extracted features and overall image aesthetics. Instead of directly feeding the features to classifiers, we will focus on the individual relationships between the features and aesthetics to model a no-reference image aesthetics metric based on deep learning. We plan to use AVA dataset introduced in \cite{Murray2012} to evaluate the aesthetics metrics because of the image variety, aesthetics scores and rich annotations.

{

\section{Introduction}
\label{sec:intro}
\vspace{-0.1in}
Everyday, we are exposed to various images and video thanks to Facebook, Flickr, Youtube, Instagram and others. The content in these websites or applications are provided by the users. While uploading the multimedia content, files need to satisfy basic constraints such as format, size and resolution. However, these social media platforms do not assess the quality of multimedia content. Subjective quality evaluation is by far the best way to asses the quality of multimedia. However, it requires extensive amount of time and labor. Therefore, objective quality metrics are used to estimate subjective quality.

Most of the quality assessment methods estimate  the quality by calculating the degradations in the images and videos. Fidelity-based metrics calculate the accuracy of the processed content with respect to the original content whereas structural metrics model the perceived quality of visual data by considering the Human Visual System (HVS). However, fidelity and structure-based metrics are not sufficient to estimate the quality of experience for the end user. We claim in our work that we also need to consider the aesthetics within images and videos. Hence, in our work, we develop image quality measures that incorporate aesthetics as well as other structure-based and statistical models.

The authors in~\cite{Datta2006} proposed a computational approach to study aesthetics in photographic images. They studied aesthetics as a machine learning problem by extracting low-level features based on rules of thumbs in photography, common intuition and rating patterns. In~\cite{Hasler2003}, authors designed a colorfulness index to asses the quality rather than fidelity. Colorfulness index quantifies the colorfulness in natural images to estimate perceived quality by using the color distribution in the CIELab color space. In addition to the colorfulness feature, brightness, contrast, saturation and saliency were also used to asses the beauty rating of videos in \cite{Yildirim2013}. In \cite{Dhar2011}, the authors used composition, content and sky-illumination as high level attributes to predict aesthetics and interestingness.

Instead of focusing on the entire image, the authors in \cite{Luo2008} extracted subject regions using blind motion deblurring \cite{Levin2006} to detect areas that draw the most attention of HVS. The authors in \cite{Tang2013} used hue and scene composition features as global features whereas dark channel, face region and complexity as regional features. Sharpness, colorfulness, luminance, color harmony and blockiness were used in \cite{Moorthy2010} as low-level features to model visual aesthetic appeal. Composition-specific features such as relative foreground position and visual weight ratio were used in \cite{Bhattacharya2010} to asses photo quality and perform semi-automatic enhancement based on visual aesthetics. Instead of hand-crafting features that highly correlate with photographic practices and techniques, the authors in \cite{Marchesotti2011} used generic image descriptors such as GIST \cite{Oliva2001} and BOV (\cite{Csurka2004}, \cite{Sivic2003}) to asses aesthetics of images.

It is not straightforward to describe aesthetics. Therefore, we need to focus on the judgement of subjects. The authors in \cite{Luo2008} generated a video database by selecting videos from YouTube. The database contains 4000 high quality professional movie clips and 4000 low quality amateurish clips. The quality of the videos was assessed per frame basis and the average was computed to asses the quality of videos. The authors also used MSN Live Search to search for images and volunteers ranked the images on a scale between 1 and 5. A photography database with peer-rated aesthetics scores ranging from 1 to 7 was provided in \cite{Photonet}. Similarly, \cite{DPChallange} provided a large photo database with peer ratings based on quality ranging from 1 to 10. Authors in \cite{Datta08} randomly collected large samples from  \cite{Alipr}, \cite{DPChallange} , \cite{Photonet} and \cite{Terragalleria} that were annotated with aesthetics, quality, liking and emotion scores. Around 1 million images crawled by Flickr with textual tags, aesthetics annotations, and EXIF meta-data were provided in \cite{CLEF2010}. A large set of standardized, emotionally-evocative color photographs were provided with a wide range of semantic categories in \cite{IAPS}. Authors in \cite{Murray2012} generated a large scale database with score distributions, semantic and style labels and rich annotations including aesthetics.

In this work, we focus on the binary classification of the images based on aesthetics quality. We examine the descriptors used in the aesthetics image quality  literature  as well as  other commonly used image descriptors.  In section \ref{sec:literature}, we introduce state of the art computational aesthetics descriptors. Geometric descriptors are discussed in section \ref{sec:genericgeometric} and color descriptors are explained in \ref{sec:genericcolor}. We examine hybrid descriptors in section \ref{sec:generichybrid} and visual dictionary approach in section \ref{sec:genericvisual}. We compare the descriptors in section \ref{sec:comparison} and conclude our discussion in section \ref{sec:conclusion}.

\vspace{-0.1in}

\section{Aesthetics Descriptors }
\label{sec:mainbody}
\vspace{-0.1in}
  We can measure the success of the computational models  by how good they can estimate the average subjective scores. From the image sets referred in section \ref{sec:intro}, we use the \texttt{CUHK} database collected by the authors in \cite{Ke2006}. The images in the database are obtained from the photo contest website DPChallenge~\cite{DPChallange} along with the user ratings. From the obtained 60,000 images, the top 10\% images are selected as \texttt{good} and the bottom 10\% images are selected as \texttt{bad} images. Half of the image set is randomly selected to be used for training with labels and the other half is used for the classification tests. In the following sections, we briefly introduce the descriptors and examine their classification performances.

\subsection{State of the art descriptors }
\label{sec:literature}
Ke at al. \cite{Ke2006} designed aesthetics features using spatial distribution of edges, color distribution, hue count, blur, contrast and brightness. Datta et al. \cite{Datta2006}  used exposure of light, colorfulness, saturation, hue, rule of thirds, familiarity measure, wavelet based texture feature, size and aspect ratio, region composition, depth of field and shape convexity to asses image aesthetics. Tong et al. \cite{Tong2004} implemented a black-box approach by generating a set of low-level features and fusing these features using learning algorithms. In addition to extracting global features, Luo and Tang \cite{Luo2008} extracted subject regions to obtain local features of foreground and background. They calculated clarity contrast, lighting, simplicity, composition geometry and color harmony to model the aesthetics. Marchesotti et al. \cite{Marchesotti2011} used generic features instead of hand-crafted features to perform aesthetics-based classification. SIFT and color features were extracted and a global model was generated using Gaussian Mixture Model (GMM). Bag of Words (BoW) and Fisher Vector (FV) were used to obtain the statistics of feature distributions to train a Support Vector Machine (SVM) classifier.

\begin{figure}[tbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/literature_v2.pdf}
\vspace{-0.15in}
\caption{Classification accuracy of state of the art methods}
\vspace{-0.2in}
\label{fig:Literature}
\end{figure}

We summarize the classification accuracy of state of the art methods in Table~\ref{fig:Literature}. Hand-crafted global features can reach to a classification accuracy up to $76\%$ with Ke et al. \cite{Ke2006}. Local features proposed by Luo and Tang \cite{Luo2008} leads to an accuracy up to $93\%$. Generic features used by Marchesotti et al. \cite{Marchesotti2011} result in an  accuracy between $81.4\%$ and $89.9\%$. In the rest of the simulations, we use L1 soft-margin SVM classifier. We experimented other classifiers and observed that classification accuracy did not change significantly when we had a large set of training and test images.

\vspace{-0.10in}

\subsection{Geometric Descriptors }
\label{sec:genericgeometric}
GIST is a holistic representation of an image to model the shape of the scene \cite{Oliva2001}. Scene representation is classified with respect to naturalness, openness, roughness, expansion and ruggerdness. GIST uses the local and global energy spectrum to quantify the introduced metrics in the scene representation. We use three different configurations of the GIST features by varying the number of orientations per scale and number of blocks. GIST(16) and GIST(32) correspond to the configuration where we use an unlocalized energy spectra by setting the number of block to \texttt{1}. GIST(512) corresponds to the case where the number of blocks is set to \texttt{4}. Orientations per scales are all set to \texttt{8} for GIST(32) and GIST(512) where orientation scales are set to \texttt{4} for GIST(16). SIFT divides the image into \texttt{4x4} grids of cells and calculates histogram of image gradient directions as explained in \cite{Lowe2004}. GMM approximates the distributions by weighted sum of Gaussian models. In addition to being used as a feature, it is also used to generate the visual dictionary in section \ref{sec:genericvisual}. Maximally stable extremal regions (MSER) thresholds the image in the intensity channel. Threshold is swept from black to white to detect the connected areas that are unchanged over a large set of thresholds \cite{MSER2002}. Difference of Gaussians (DOG) convolves the original image with Gaussian kernels and subtracts the blurred images from each other. Difference of blurred images contains band-pass details that are used as image descriptors. We also detect corners and blobs to represent images using Hessian, Harris and Laplace operators. Implementation of most of the geometric descriptors are provided with VLFeat package which is an open and portable library of computer vision algorithms \cite{Vlfeat2008}.

\begin{figure}[tbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/geometric_v2.pdf}
\vspace{-0.1in}
\caption{Classification accuracy of Geometric Descriptors}
\vspace{-0.2in}
\label{fig:Geometric}

\end{figure}

Classification results for geometric descriptors are given in Figure \ref{fig:Geometric}. In here, we calculate the geometric descriptors by computing the average of each feature dimension over the whole image. We obtain the most accurate classification with $67.7\%$ in DOG, $67.2\%$ in SIFT and $67.1\%$ in HessianLaplace.
Generic geometric descriptors inherently contain information related to the spatial complexity and composition. Some of the geometric descriptors perform better than the others but none of them is sufficient for aesthetics classification since they do not focus on the basic dimensions of the aesthetics.

\vspace{-0.10in}

\subsection{Color Descriptors}
\label{sec:genericcolor}

Color descriptors are designed according to four main constraints: photometric robustness, geometric robustness, photometric stability and generality \cite{Khan2013}. In order to satisfy these constraints, color descriptors should be invariant to shadow, shading, light source configuration, view point, orientation and image quality. However, it is not possible to design descriptors that can satisfy all the constrains. Since image aesthetics is also influenced by these factors, we can use color descriptors as aesthetics metrics.

Color naming is introduced in \cite{ColorNaming2009} as an image descriptor, which calculates the color distributions similar to the bag of words approach. Relative locations of pixels do not effect the distribution since this approach only focuses on the color distribution of the pixels in the region of interest. It is originally used to assign linguistic color labels to image pixels and the main objective is to predict the color category that humans would perceive given a color measurement. In practice, color naming descriptor is a 11-D vector where each dimension corresponds to the distribution of main colors that can be sorted as follows: black, blue, brown, grey, green, orange, pink, purple, red, white and yellow. We also use the color naming method described in \cite{ParametricColorNaming2008}. Authors use a fuzzy $k$-means algorithm to obtain color labels from Munsell book of color. Color descriptor consists of member functions which map color elements to $[0,1]$ interval and the abbreviation JOSA is used to represent this descriptor in the results .

In addition to the distribution of colors, we can also consider the relative locations of the color pixels. We use discriminative color descriptor introduced in \cite{Khan2013} to cluster the color pixels in compact representations. Color pixels are clustered by maximizing the discriminative power using an information theocratical approach. We also use the color descriptors in the opponent color space as described in \cite{Weijer06}. Color descriptors are designed in a similar way to SIFT by calculating the histogram of gradients in hue. The hue color descriptor performs poorly when the saturation is low. In case of low saturation, we can use opponent color angle \cite{Weijer06}.

We use the default version of the color descriptors defined in \cite{ColorCodes}. Color descriptor matrix is composed of three rows where each row corresponds to the metric calculated over three constant regions. In addition, we take the average of the metric over three regions and use it as an additional descriptor which is shown with a suffix $(1x11)$. In case of discriminative color, we also calculate the color descriptors for a color dictionary size of $25$ and $50$.

\vspace{-0.1in}

\begin{figure}[htbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/color_v1.pdf}
\vspace{-0.2in}
\caption{Classification accuracy of Color Descriptors}
\label{fig:Color}

\end{figure}

Color naming (1x11) results in the highest classification accuracy with $73.6\%$ followed by JOSA(1x11) with $72.1\%$. When we compare with the generic descriptors, color descriptors perform better. In the literature, variations of color distribution and harmony are commonly used as hand-crafted features to model aesthetics as it is mentioned in section \ref{fig:Literature}.

\vspace{-0.10in}

\subsection{Hybrid Descriptors}
\label{sec:generichybrid}
Color, geometric and aesthetics descriptors analyze the images in different aspects and they can be complementary to each other for classification. However, they can also contradict with each other in terms of classification decisions. We combine some of the descriptors defined in the previous parts to obtain a hybrid descriptor. Classification accuracies of the hybrid descriptors are shown in Figure \ref{fig:Hybrid}.

\begin{figure}[htbp!]
\centering
\includegraphics[width=0.80\linewidth]{Figs/hybrid_v3.pdf}
\vspace{-0.1in}
\caption{Classification accuracy of Hybrid Descriptors}
\label{fig:Hybrid}
\end{figure}

\begin{figure*}[t]
\vspace{-0.1in}
\centering
\includegraphics[width=0.80\linewidth]{Figs/training_v3.pdf}
\vspace{-0.3in}
\caption{Training pipeline}
\label{fig:Training}
\vspace{-0.2in}
\end{figure*}

We use Color naming (1x11) as the color descriptor which is abbreviated as CN and SIFT is used as the geometric descriptor. When CN and SIFT are combined together, classification accuracy gets lower than the accuracy of CN but higher than the accuracy of SIFT with $70.1\%$. Optimal scale (OS) is a feature introduced to quantify the amount of low frequency content in the image. Classification ratio increases by $1\%$ when OS is added to the descriptor. We add exposure of light, hue, rule of thirds, wavelet-based texture and size and aspect ratio features introduced by Datta et al. \cite{Datta2006} to our descriptor. Classification accuracy increases up to $75.7\%$. Finally, we add   brightness, saturation, average color and colorfulness features proposed by Susstrunk et al. \cite{Hasler2003} and \cite{Yildirim2013}. Our hybrid descriptor has a classification accuracy of $75.9\%$. Instead of training with the whole set, we train with the first $100$ good and bad images to experiment the effect of training set size. Classification accuracy drops from $75.9\%$ to $70.2\%$.

\vspace{-0.18in}

\subsection{Visual Dictionary Approach}
\vspace{-0.05in}

\label{sec:genericvisual}

In the previous sections, we extract features and combine them in a vector to form a descriptor. However, we do not use the distribution of the features. In contrast, in this section, we generate a visual dictionary to consider the distributional statistics of the features. The training pipeline including visual dictionary generation is shown in Figure \ref{fig:Training}.

We extract features from two different classes of images and these features are fed to a universal feature model to obtain a visual dictionary. Training features and universal model parameters are fed to a descriptor extraction module to obtain class I and class II descriptors. We train the classifier with the labeled descriptors to obtain a classifier model. We use SIFT, DOG, Color naming (1x11) and JOSA because they have the highest classification accuracy based on our experiments from previous sections. In addition to the default SIFT, we perform singular value decomposition to keep the first $N$ eigenvalues and remove the rest to reconstruct the feature vector. We experimented with different $N$ values and $30$ produced the highest classification accuracy. Gaussian Mixture model (GMM) is used as the universal feature model and Fisher Vector is preferred as the descriptor. In our simulations, we vary the number of Gaussians in the GMM and the training set size. For each feature, we perform simulations at least for $14$ different configurations and up to $22$. Configurations that lead to highest classification accuracy are given in Table \ref{tab:1}.

\begin{table}[htb]
\vspace{-0.10in}
\centering
\caption{Classification accuracy for the dictionary approach  \label{tab:1}}

 \begin{tabular}{|c|c|c|c|} \hline
\textbf{Features}&  \textbf{Number of} & \textbf{Training} & \textbf{Classification } \\
\textbf{Types}&  \textbf{Gaussians} & \textbf{Set Size} & \textbf{Accuracy (\%)} \\\hline
SIFT(SVD) &  \texttt{200} & \texttt{100} & \texttt{75.5}\\\hline
SIFT &  \texttt{200} & \texttt{100} & \texttt{74.0}\\\hline
CN&  \texttt{5} & \texttt{6000} & \texttt{72.6}\\\hline
DoG&  \texttt{2} & \texttt{6000} & \texttt{69.7}\\\hline
JOSA&  \texttt{5} & \texttt{6000} &  \texttt{67.6}\\\hline
\end{tabular}
\vspace{-0.20in}
\end{table}

\vspace{-0.15in}
\subsection{Descriptor Comparison}
\vspace{-0.10in}

\label{sec:comparison}

We observe that state of the art descriptors using local information leads to classification ratios up to $90\%$. Luo and Tong \cite{Luo2008} used hand-crafted features and Marchesotti et al. \cite{Marchesotti2011} used generic features to obtain high classification accuracy. As it was claimed in \cite{Marchesotti2011}, generic features can perform as good as hand-crafted features. However, examining local features in addition to global features have a more significant effect on the classification results than feature selection. The maximum classification ratio we obtain using geometric descriptors is $67.7\%$ and it is $73.6\%$ using color descriptors. Hybrid descriptors lead to $75.9\%$ and the dictionary approach leads to $75.5\%$ at most. Basic generic features such as color and geometric can perform as well as the state of the art global computational approaches. However, they do not perform as well as the ones that take spatial characteristics into account by using subject region extraction or spatial pyramid. The main disadvantage of the regional methods comes from the time and memory complexity. Subject region extraction used in \cite{Luo2008} is an exhaustive approach that requires significant amount of computational time and the spatial pyramid originally introduced in \cite{SpatialPyramid2006} requires significant amount of memory (Approximately 250GB of memory is required to store the extracted features of $1$ dataset out of $4$ in the CUHK dataset ).

\vspace{-0.1in}

\section{Conclusion }
\label{sec:conclusion}
\vspace{-0.1in}

In this paper, we compare generic, hand-crafted and aesthetics descriptors to examine the classification performance of image aesthetics. We have shown that basic generic descriptors can perform as well as the state of the art hand-crafted global descriptors. However, both generic and hand-crafted features are limited in terms of classification when we do not consider the spatial distribution of features. Spatial pyramid and subject region extraction are the main factors that lead to high classification accuracies in the literature. But they require significant amount of computational time and memory. In our future work, we will examine the correlation between the extracted features and overall image aesthetics. Instead of directly feeding the features to classifiers, we will focus on the individual relationships between the features and aesthetics to model a no-reference image aesthetics metric based on deep learning. We plan to use AVA dataset introduced in \cite{Murray2012} to evaluate the aesthetics metrics because of the image variety, aesthetics scores and rich annotations.

{

redpajama_set_name: RedPajamaArXiv


San Diego Dance Connect gathered for a Happy Hour event on Saturday, October 22, 2016 to network as a community and support 40 NORTH Dance Film Festival at Art Produce.
The 2nd Annual 40 North Dance Film Festival received 106 submissions from 19 countries and is a true representation of this innovative art form that blends dance and camera choreography to create unique visual experiences. 36 film selections will screen in three events, each in a different location, over two weekends. Dance film is the next prominent platform for dance and an essential venue of exploration for movement artists.
Thanks to all that joined us for this SDDC event!


The list of economists betting on a rate hike this month keeps getting smaller.
Recently, Larry Summers, The World Bank, the IMF, and even the New York Times editorial board have advised the Fed to keep rates at a nine-year low when it meets next week.
In a note Wednesday, Deutsche Bank's Joe LaVorgna added his voice to the argument.
Markets are volatile. The S&P 500 fell 11% in six days during August, double the amount it's historically dropped before a tightening cycle begins. The dollar is surging, as measured by the Fed's trade-weighted index. This may put more pressure on net exports and keep the lid on inflation. Markets aren't expecting a hike this month, and the Fed won't want to surprise everyone. On Tuesday, Fed fund futures were reflecting a 30% chance the Fed would move, down from 54% about a month ago. FOMC members including Richard Dudley and John Williams have recently suggested that the economy is showing the Fed an amber light, not green. The Fed still has its October and December meetings to observe any effects of the recent market plunge, and see if volatility settles down. The Fed's 'dot plot' showed that it anticipates raising rates by 25 basis points either once or twice this year. The leaked staff projections also reflected this. And so the Fed won't lose credibility with markets by waiting. Inflation has been running below the Fed's target for three years.


This past week, I had the privilege of hearing French President Emmanuel Macron's speech to a joint session of Congress. In my previous meetings with Macron at CES® in Las Vegas and CES Unveiled Paris, I was impressed by his drive, eloquence and, above all, by his vision for France in the coming decades. Wednesday's speech only confirmed my original impression. Macron is a leader for our times, and President Donald Trump must seize the opportunity to partner with him to forge a new era of global prosperity and stability.
Despite differences in style and age, Macron and Trump are alike in many ways. Both leaders are entrepreneurs and disruptors. And both leaders are tackling the tough reforms to make their countries great. For Macron, this has played out in recent confrontations with rail workers, who are striking in protest of Macron's new labor reforms — which are desperately needed in France's stagnant job market. For Trump, it's been his commitment to deep, substantive changes to our tax system and long overdue regulatory reform.
This makes the two men ideal partners on the global stage — and, in my view, they are the Reagan and Thatcher of our time.At home, Reagan and Thatcher both cut back on choking government rules so entrepreneurs and innovators could drive growth and create jobs — just as Macron has done in France and Trump has done here. And abroad, the Great Communicator and the Iron Lady teamed up to forge a freer, more peaceful world — just as Trump and Macron can do, if they put their minds to it.
We need to stand firm by our historic allies like France in these turbulent times. The world is dividing into two different models of citizenship and governance. Some embrace a liberal democracy, and citizens are given the liberty to select the faith, jobs, education and leaders of their choice. Others are centralized, totalitarian nations that stifle innovation, creativity, and the free flow of information.
Macron's speech highlighted "attacks on democracy through the rise of illiberalism and the destabilization of our international community by new powers and criminal states." He went on to advocate for the active involvement of the U.S. on the world stage, asking the U.S. to "preserve and reinvent" multilateralism for a new generation. And his gift to President Trump — an oak tree from Belleau Wood in France, where many U.S. Marines died in combat in World War I a century ago — symbolizes the power and purpose of U.S. and French cooperation.
We need that power and purpose now more than ever.
China is on the other side of this global divide, and it continues to make gains on the West's lead in innovation. The Chinese government has released a plan to eclipse all other nations in artificial intelligence, announcinga $2 billion research park in Beijing. And, due to unfair trade practices, they're able to take U.S. trade secrets and intellectual property and profit off of them.
With a threat like this on the horizon, Americans need Macron and Trump to work together. Yes, the two men might hold vastly different opinions on topics such as climate change or health care — but their similarities run far deeper than their differences. A commitment to democracy, prosperity, and liberty are the lifeblood of both our countries — the values for which we have fought, lived, and died together.
My hope is that Macron and Trump will continue to deepen their partnership in the coming years, working together to create a world where innovation can thrive, economies can grow, and freedom can flourish.


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Blu-Ray Review: 'Batman v Superman: Dawn of Justice – Ultimate Edition'
By Kyle Wilson July 19, 2016
Batman v Superman: Dawn of Justice was easily one of the most divisive films of 2016, and still continues to rile up nerds and movie fans everywhere. Now with the release of Batman v Superman: Dawn of Justice Ultimate Edition there's even more to debate as director Zack Snyder's original vision for the film is revealed in a vastly superior film compared to the theatrical cut.
You can read my initial review of the standard theatrical cut here and the Ultimate Edition adds an additional 30 minutes to the already hefty original 153 minutes – and yet, somehow it doesn't feel like a longer movie. It's quite clear that there was some heavy editing going on from the studio to trim down the final theatrical film so it would get to the action faster, but unfortunately it made many things – like Lex Luthor's (Jesse Eisenberg) conspiracy – make very little sense.
Not only does the Ultimate Edition make the villain look better, it also does the same for our heroes. Clark Kent/Superman (Henry Cavill) is shown being much more Superman-like and actually caring for people in ax excised investigative B-plot, as well as extended scenes after the Capitol bombing, where Superman saves people from the rubble rather than just immediately flying away.
The Bat-branding that Batman (Ben Affleck) was burning onto criminals also played a much bigger role in the conspiracy for the Ultimate Edition, while also revealing itself to being a key factor in Luthor's plot. Batman also earns the upgraded R-rating for some truly bloody and brutal fights scenes. Personally, I didn't feel like those extended scenes were necessary, instead just making Batman look like a violent killer – but the scenes are pretty incredible to watch.
The perplexing Africa scenes in the theatrical cut also are expanded and shown the way Snyder originally intended, creating an extremely complex and interesting plot point for Luthor to discredit Superman to public. Even the much talked about Jimmy Olsen cameo works in a way that I didn't think it would without seeming too "blasphemous" for the character.
While I will say that the Ultimate Edition isn't going to sway anyone that originally hated the film, but with all of Snyder's original scenes restored, Batman v Superman: Dawn of Justice is an impressively complex deconstruction of these two legendary characters in a modern world. The film is extremely mature in content and themes and should have been applauded upon release for providing a thought-provoking superhero film with adult moral quandaries, but instead audiences left frustrated and confused by a hacked up version of the film.
Batman v Superman: Dawn of Justice Ultimate Edition also has a fantastic array of special features to go along with the much more satisfying version of the film. Not only do you get Snyder's cut of the film, the theatrical cut is also provided in addition to a Digital HD Ultraviolet version of the film to watch on the go.
Here's a breakdown of all the other features that go into impressive depth with tremendoues looks behind the film:
* Uniting the World's Finest
* Gods and Men: A Meeting of Giants
* The Warrior, The Myth, The Wonder
* Accelerating Design: The New Batmobile
* Superman: Complexity & Truth
* Batman: Austerity & Rage
* Wonder Woman: Grace & Power
* Batcave: Legacy of The Lair
* The Might and The Power of A Punch
* The Empire of Luthor
* Save the Bats
Probably the feature of most note to fans with be Uniting the World's Finest – which not only includes interviews with just about everyone behind the camera, but also every single member of the Justice League including Ben Affleck, Henry Cavill, Gal Gadot, Ezra Miller, Jason Momoa, and Ray Fisher.
Many of the other features are also interesting, but provide more of a history of the characters like Batman and Superman in Gods and Men: A Meeting of Giants, Wonder Woman in The Warrior, The Myth, The Wonder and Lex Luthor in The Empire of Luthor. Everything is still well done for fans in the know, but a credit cram session for viewers only familiar with live-action versions of the characters.
Superman: Complexity & Truth and Batman: Austerity & Rage talk about the fashion of Clark Kent and Bruce Wayne in and out of costume, and what each outfit says about the characters themselves such as Batman's bullet-ridden, battle-scarred suit that was designed to show years of use.
Gear heads and Bat-fans alike will really dig Accelerating Design: The New Batmobile for its fascinating look at the vehicle's conception all the way up to the test track and debut on set for the film. Save the Bats is really the only feature that seems a bit out-of-place, but it's for a good cause that the movie supported. The cast and crew helped build "homes" for bats to help prevent them from contracting a disease that kills millions of bats every year.
With hours of meticulously gathered, quality supplemental features and an unquestionably better version of the film, Batman v Superman: Dawn of Justice Ultimate Cut is the version fans deserved and needed upon the film's original theatrical release giving consumers the only version needed for your home video collection.
Wanna support The Nerd Repository? Click the link below and buy Batman v Superman: Dawn of Justice Ultimate Edition through our Amazon Store!
batman v superman: dawn of justice ultimated edition
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Features, Selections: 45 Drink, Columns and Selections: 5 Trays - 9 Columns per Tray, Capacity: 360 - 405 items depending on package size, Heavy Duty Welded All Steel Construction, MDB Protocol for, Coin Changer and Bill Acceptor, Glass Front Live Display, Multiple Pricing From $0.05 to $99.95, Automatic Service Diagnostics, Secure Anti-Theft Locking System, Ideal for Indoor or Outdoor Environment. You are buying a used Dixie Narco model 5591, 12oz cans and 20 oz bottle as well as 16 oz energy drinks Vending Machine. This machine is a multi-priced meaning. The unit has 45 different selections and accepts $1/$5 bills and coins with a high capacity coin mech. Refurbishing Process: Soda machines; we give these machines a general cleaning with our pressure washer. The machines are cleaned inside and out then sanded, painted. Rebuilt coin mechs and dollar bill validators are then installed. New graphics are installed on the front of the machine. A final checkout is done with coins and dollar bills to ensure proper operation. Every machine includes a lock and two keys for our customers' convenience. Actual machine looks similar to the machine in the picture. Cold drinks Graphics on front of machine. These machines are A-rated, completely refurbished machines. These machine vend 400+ 12 oz cans. These are not 'club store machines' and current models of this brand of machines sells for over $6800 new. Used by professional vending machine operators.