[null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.google.com/mobile/images/labs/goggles/goggles_landmark.jpg", "src": "http://www.google.com/mobile/images/labs/goggles/goggles_landmark.jpg", "formatted_filename": "goggles landmark", "alt_text": "Goggles results for a landmark scan.", "rendered_width": 471, "rendered_height": 206}, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.google.com/mobile/images/labs/goggles/goggles_book.png", "src": "http://www.google.com/mobile/images/labs/goggles/goggles_book.png", "formatted_filename": "goggles book", "alt_text": "Goggles results for a book scan.", "rendered_width": 469, "rendered_height": 206}, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.google.com/mobile/images/labs/goggles/goggles_contact.png", "src": "http://www.google.com/mobile/images/labs/goggles/goggles_contact.png", "formatted_filename": "goggles contact", "alt_text": "Goggles results for contact information scan.", "rendered_width": 474, "rendered_height": 206}, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.google.com/mobile/images/labs/goggles/goggles_artwork.png", "src": "http://www.google.com/mobile/images/labs/goggles/goggles_artwork.png", "formatted_filename": "goggles artwork", "alt_text": "Goggles results for an artwork scan.", "rendered_width": 474, "rendered_height": 206}, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.google.com/mobile/images/labs/goggles/goggles_wine.jpg", "src": "http://www.google.com/mobile/images/labs/goggles/goggles_wine.jpg", "formatted_filename": "goggles wine", "alt_text": "Goggles results for a wine scan.", "rendered_width": 474, "rendered_height": 206}, null, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://www.codeproject.com/KB/web-image/RbmBinaryImage/RbmBinaryImageBarCode.jpg", "src": "http://www.codeproject.com/KB/web-image/RbmBinaryImage/RbmBinaryImageBarCode.jpg", "formatted_filename": "RbmBinaryImageBarCode", "rendered_width": 458, "rendered_height": 467}, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://web.mac.com/mccanns/Site/Xerox_Distinguished_Lecturer_files/droppedImage.jpg", "src": "http://web.mac.com/mccanns/Site/Xerox_Distinguished_Lecturer_files/droppedImage.jpg", "formatted_filename": "droppedImage", "rendered_width": 473, "rendered_height": 353}, null, null, null, null, null, null, null, null, null, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://3.bp.blogspot.com/_7ZYqYi4xigk/SxWaGJ-X7-I/AAAAAAAAFCA/d_zc3svisVw/s400/ms1.png", "src": "http://3.bp.blogspot.com/_7ZYqYi4xigk/SxWaGJ-X7-I/AAAAAAAAFCA/d_zc3svisVw/s400/ms1.png", "formatted_filename": "ms"}, null, null, null, null, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://2.bp.blogspot.com/_mEhPqpXDNKY/TD9eZYzrMRI/AAAAAAAABOk/lZxzgQDvYtY/S1600-R/lowlevel.jpg", "src": "http://2.bp.blogspot.com/_mEhPqpXDNKY/TD9eZYzrMRI/AAAAAAAABOk/lZxzgQDvYtY/S1600-R/lowlevel.jpg", "formatted_filename": "lowlevel", "rendered_width": 231, "rendered_height": 36}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://2.bp.blogspot.com/-CtHDzakZq38/U0KU_X64WcI/AAAAAAAAJ3o/d6FDhafs44Y/s1600/simple.png", "src": "http://2.bp.blogspot.com/-CtHDzakZq38/U0KU_X64WcI/AAAAAAAAJ3o/d6FDhafs44Y/s1600/simple.png", "formatted_filename": "simple", "alt_text": "SIMPLE Descriptors", "rendered_width": 248, "rendered_height": 95}, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://4.bp.blogspot.com/_mEhPqpXDNKY/TD9HSilA_VI/AAAAAAAABNc/jPZPQ98jPCU/S1600-R/ccds.jpg", "src": "http://4.bp.blogspot.com/_mEhPqpXDNKY/TD9HSilA_VI/AAAAAAAABNc/jPZPQ98jPCU/S1600-R/ccds.jpg", "formatted_filename": "ccds", "alt_text": "Compact Composite Descriptors", "rendered_width": 248, "rendered_height": 86}, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://1.bp.blogspot.com/_mEhPqpXDNKY/TD9HhrIZlHI/AAAAAAAABNk/eDWUJjd22MI/S1600-R/mpeg7.jpg", "src": "http://1.bp.blogspot.com/_mEhPqpXDNKY/TD9HhrIZlHI/AAAAAAAABNk/eDWUJjd22MI/S1600-R/mpeg7.jpg", "formatted_filename": "mpeg", "alt_text": "MPEG-7 Descriptors", "rendered_width": 248, "rendered_height": 86}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://4.bp.blogspot.com/_mEhPqpXDNKY/TD9HmkMAO7I/AAAAAAAABNs/YVVKiCt87Xs/S1600-R/lire.jpg", "src": "http://4.bp.blogspot.com/_mEhPqpXDNKY/TD9HmkMAO7I/AAAAAAAABNs/YVVKiCt87Xs/S1600-R/lire.jpg", "formatted_filename": "lire", "rendered_width": 248, "rendered_height": 86}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://3.bp.blogspot.com/-l4n269VW54g/Tl62QrK04LI/AAAAAAAABdg/zg-z4cSPX9o/s1600/ccd.jpg", "src": "http://3.bp.blogspot.com/-l4n269VW54g/Tl62QrK04LI/AAAAAAAABdg/zg-z4cSPX9o/s1600/ccd.jpg", "formatted_filename": "ccd", "rendered_width": 248, "rendered_height": 349}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://1.bp.blogspot.com/-l4VfKNrOhMc/ULd0vE6To1I/AAAAAAAAHBc/B359YJBubho/s300/personal.jpg", "src": "http://1.bp.blogspot.com/-l4VfKNrOhMc/ULd0vE6To1I/AAAAAAAAHBc/B359YJBubho/s300/personal.jpg", "formatted_filename": "personal", "rendered_width": 242, "rendered_height": 240}, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://2.bp.blogspot.com/-tl8GdksRGPo/Tihi_-K0MUI/AAAAAAAABbw/vzEwDBds0mc/s1600/Untitled%2B-%2B1.jpg", "src": "http://2.bp.blogspot.com/-tl8GdksRGPo/Tihi_-K0MUI/AAAAAAAABbw/vzEwDBds0mc/s1600/Untitled%2B-%2B1.jpg", "formatted_filename": "Untitled B B", "rendered_width": 231, "rendered_height": 36}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://2.bp.blogspot.com/_mEhPqpXDNKY/TD9G0PXkMbI/AAAAAAAABNU/sEBJvP4J8zo/S1600-R/rummager.jpg", "src": "http://2.bp.blogspot.com/_mEhPqpXDNKY/TD9G0PXkMbI/AAAAAAAABNU/sEBJvP4J8zo/S1600-R/rummager.jpg", "formatted_filename": "rummager", "alt_text": "Img(Rummager)", "rendered_width": 248, "rendered_height": 170}, null, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://3.bp.blogspot.com/_mEhPqpXDNKY/TD9F5fyI9lI/AAAAAAAABNE/7OUt7orWQ34/S1600-R/anaktisi.jpg", "src": "http://3.bp.blogspot.com/_mEhPqpXDNKY/TD9F5fyI9lI/AAAAAAAABNE/7OUt7orWQ34/S1600-R/anaktisi.jpg", "formatted_filename": "anaktisi", "alt_text": "Img(Anaktisi)", "rendered_width": 248, "rendered_height": 170}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://1.bp.blogspot.com/_mEhPqpXDNKY/TD9e2Fg3q0I/AAAAAAAABOs/EQ-DRn6hFIk/S1600-R/tutorials.jpg", "src": "http://1.bp.blogspot.com/_mEhPqpXDNKY/TD9e2Fg3q0I/AAAAAAAABOs/EQ-DRn6hFIk/S1600-R/tutorials.jpg", "formatted_filename": "tutorials", "rendered_width": 231, "rendered_height": 36}, null, {"document_url": "http://savvash.blogspot.com/2009/12/", "unformatted_src": "http://3.bp.blogspot.com/_mEhPqpXDNKY/TD9e9T-IT4I/AAAAAAAABO0/Tii4ZAIRK5M/S1600-R/hot.jpg", "src": "http://3.bp.blogspot.com/_mEhPqpXDNKY/TD9e9T-IT4I/AAAAAAAABO0/Tii4ZAIRK5M/S1600-R/hot.jpg", "formatted_filename": "hot", "rendered_width": 231, "rendered_height": 36}, null, null, null]

Best Wishes for 2010
 
CFP: The Second International Workshop on Mobile Multimedia Processing (WMMP'10)
 The Second International Workshop on Mobile Multimedia Processing will be held in Istanbul, Turkey, on August 22, 2010, in conjunction with the 20th International Conference on Pattern Recognition (ICPR 2010). This is the follow-up of the successful First International Workshop on Mobile Multimedia Processing held in conjunction with 19th ICPR in Tampa, Florida, 2008. The motivation of this workshop series is to timely address the challenges in applying advanced pattern recognition, signal processing, computer vision and multimedia techniques to mobile systems, given the proliferating market of mobile and portable devices that have been widely spreading in both consumer (e.g. smartphones such as iPhone, music, mobile TV, digital cameras, HDTV) and industrial markets (e.g. control, medical, defense etc.).

One aim of the workshop is to bring together researchers in pattern recognition as well as in mobile interaction, ubiquitous computing, and multimedia. The intended audiences of this workshop are researchers in traditional pattern recognition and media processing techniques wanting to extend their work in the mobile domain, and those researchers in mobile interaction, ubiquitous computing, and cross related fields wanting to explore latest achievements in the pattern recognition field to expand their work.

control, medical, defense). The proposed scope of this workshop includes, but is not limited to, the following areas:

- Mobile speech, image and video processing

- Surveillance, biometric, authentication and security technologies in mobile environment

- Mobile visual search

- Mobile image retrieval

- Mobile augmented reality browsers

- Mobile video streaming

- Multimodal pedestrian navigation systems

- Multimedia applications for automotive systems

- Mobile navigation, content retrieval, authentication

- Pervasive computing / context aware methodology and application

- Multimodal interfaces and visualization for mobile devices

- Handheld augmented reality

- Personalization and recommender systems in mobile environment

- Mobile oriented media processing for communication and networking

- Medical applications and bioinformatics in mobile environment

- Entertainment applications in mobile environment

- Mobile multimedia applications in geospatial information systems


Paper submission: April 1, 2010

Author notification: May 1, 2010

ICPR early registration: May 14, 2010

Final camera-ready manuscript due: June 1, 2010



Paper, peer review, and vested interests [Guest Editorial]
 Boggs, S.

This paper appears in: Electrical Insulation Magazine, IEEE
Publication Date: November-December 2009
Volume: 25,  Issue: 6
On page(s): 3-3
ISSN: 0883-7554
Digital Object Identifier: 10.1109/MEI.2009.5313703
Current Version Published: 2009-11-20

Peer review goes back to at least 1665 with the founding of Philosophical Transactions of the Royal Society. However peer review did not become well established until the middle of the last century. For example, Einstein's 1905 papers in Annalen der Physik were not subjected to a peer review process. The purpose for which peer review was established is not clear. The probable motives include a combination of rationing of (then) very expensive journal pages, catching obvious errors, and at least some degree of quality control on what is published. The question now is "Do past models make sense in the world of the Internet?", as today, on-line published pages are essentially free and continuous on-line review and rebuttal is practical.



Scale-invariant feature transform
 Article From Wikipedia

For any object in an image, there are many 'features' which are interesting points on the object, that can be extracted to provide a "feature" description of the object. This description extracted from a training image can then be used to identify the object when attempting to locate the object in a test image containing many other objects. It is important that the set of features extracted from the training image is robust to changes in image scale, noise, illumination and local geometric distortion, for performing reliable recognition. Lowe's patented method can robustly identify objects even among clutter and under partial occlusion because his SIFT feature descriptor is invariant to scale, orientation, affine distortion and partially invariant to illumination changes. This section presents Lowe's object recognition method in a nutshell and mentions a few competing techniques available for object recognition under clutter and partial occlusion.

David Lowe's method

SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalized Hough transform. Each cluster of 3 or more features that agree on an object and its pose is then subject to further detailed model verification and subsequently outliers are discarded. Finally the probability that a particular set of features indicates the presence of an object is computed, given the accuracy of fit and number of probable false matches. Object matches that pass all these tests can be identified as correct with high confidence.

Key stages

Scale-invariant feature detection

Lowe's method for image feature generation called the Scale Invariant Feature Transform (SIFT) transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation, partially invariant to illumination changes and robust to local geometric distortion. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Key locations are defined as maxima and minima of the result of difference of Gaussians function applied in scale-space to a series of smoothed and resampled images. Low contrast candidate points and edge response points along an edge are discarded. Dominant orientations are assigned to localized keypoints. These steps ensure that the keypoints are more stable for matching and recognition. SIFT descriptors robust to local affine distortion are then obtained by considering pixels around a radius of the key location, blurring and resampling of local image orientation planes.

Feature matching and indexing

Indexing is the problem of storing SIFT keys and identifying matching keys from the new image. Lowe used a modification of the k-d tree algorithm called the Best-bin-first search method that can identify the nearest neighbors with high probability using only a limited amount of computation. The BBF algorithm uses a modified search ordering for the k-d tree algorithm so that bins in feature space are searched in the order of their closest distance from the query location. This search order requires the use of a heap (data structure) based priority queue for efficient determination of the search order. The best candidate match for each keypoint is found by identifying its nearest neighbor in the database of keypoints from training images. The nearest neighbors are defined as the keypoints with minimum Euclidean distance from the given descriptor vector. The probability that a match is correct can be determined by taking the ratio of distance from the closest neighbor to the distance of the second closest.

Lowe rejected all matches in which the distance ratio is greater than 0.8, which eliminates 90% of the false matches while discarding less than 5% of the correct matches. To further improve the efficiency of the best-bin-first algorithm search was cut off after checking the first 200 nearest neighbor candidates. For a database of 100,000 keypoints, this provides a speedup over exact nearest neighbor search by about 2 orders of magnitude yet results in less than a 5% loss in the number of correct matches.

Cluster identification by Hough transform voting

Hough Transform is used to cluster reliable model hypotheses to search for keys that agree upon a particular model pose. Hough transform identifies clusters of features with a consistent interpretation by using each feature to vote for all object poses that are consistent with the feature. When clusters of features are found to vote for the same pose of an object, the probability of the interpretation being correct is much higher than for any single feature. An entry in a hash table is created predicting the model location, orientation, and scale from the match hypothesis.The hash table is searched to identify all clusters of at least 3 entries in a bin, and the bins are sorted into decreasing order of size.

Each of the SIFT keypoints specifies 2D location, scale, and orientation, and each matched keypoint in the database has a record of the keypoint’s parameters relative to the training image in which it was found. The similarity transform implied by these 4 parameters is only an approximation to the full 6 degree-of-freedom pose space for a 3D object and also does not account for any non-rigid deformations. Therefore, Lowe used broad bin sizes of 30 degrees for orientation, a factor of 2 for scale, and 0.25 times the maximum projected training image dimension (using the predicted scale) for location. The SIFT key samples generated at the larger scale are given twice the weight of those at the smaller scale. This means that the larger scale is in effect able to filter the most likely neighbours for checking at the smaller scale. This also improves recognition performance by giving more weight to the least-noisy scale. To avoid the problem of boundary effects in bin assignment, each keypoint match votes for the 2 closest bins in each dimension, giving a total of 16 entries for each hypothesis and further broadening the pose range.

[edit] Model verification by linear least squares

Each identified cluster is then subject to a verification procedure in which a linear least squares solution is performed for the parameters of the affine transformation relating the model to the image. The affine transformation of a model point [x y]T to an image point [u v]T can be written as below where the model translation is [tx ty]T and the affine rotation, scale, and stretch are represented by the parameters m1, m2, m3 and m4. To solve for the transformation parameters the equation above can be rewritten to gather the unknowns into a column vector. This equation shows a single match, but any number of further matches can be added, with each match contributing two more rows to the first and last matrix. At least 3 matches are needed to provide a solution. We can write this linear system as where A is a known m-by-n matrix (usually with m > n), x is an unknown n-dimensional parameter vector, and b is a known m-dimensional measurement vector.

Therefore the minimizing vector is a solution of the normal equation The solution of the system of linear equations is given in terms of the matrix (ATA) − 1AT , called the pseudoinverse of A, by which minimizes the sum of the squares of the distances from the projected model locations to the corresponding image locations.

Outlier detection

Outliers can now be removed by checking for agreement between each image feature and the model, given the parameter solution. Given the linear least squares solution, each match is required to agree within half the error range that was used for the parameters in the Hough transform bins. As outliers are discarded, the linear least squares solution is re-solved with the remaining points, and the process iterated. If fewer than 3 points remain after discarding outliers, then the match is rejected. In addition, a top-down matching phase is used to add any further matches that agree with the projected model position, which may have been missed from the Hough transform bin due to the similarity transform approximation or other errors.

The final decision to accept or reject a model hypothesis is based on a detailed probabilistic model. This method first computes the expected number of false matches to the model pose, given the projected size of the model, the number of features within the region, and the accuracy of the fit. A Bayesian probability analysis then gives the probability that the object is present based on the actual number of matching features found. A model is accepted if the final probability for a correct interpretation is greater than 0.98. Lowe's SIFT based object recognition gives excellent results except under wide illumination variations and under non-rigid transformations.

Competing methods for scale invariant object recognition under clutter / partial occlusion

RIFT is a rotation-invariant generalization of SIFT. The RIFT descriptor is constructed using circular normalized patches divided into concentric rings of equal width and within each ring a gradient orientation histogram is computed. To maintain rotation invariance, the orientation is measured at each point relative to the direction pointing outward from the center.

G-RIF : Generalized Robust Invariant Feature is a general context descriptor which encodes edge orientation, edge density and hue information in a unified form combining perceptual information with spatial encoding. The object recognition scheme uses neighbouring context based voting to estimate object models.

"SURF : Speeded Up Robust Features" is a high-performance scale and rotation-invariant interest point detector / descriptor claimed to approximate or even outperform previously proposed schemes with respect to repeatability, distinctiveness, and robustness. SURF relies on integral images for image convolutions to reduce computation time, builds on the strengths of the leading existing detectors and descriptors (using a fast Hessian matrix-based measure for the detector and a distribution-based descriptor). It describes a distribution of Haar wavelet responses within the interest point neighbourhood. Integral images are used for speed and only 64 dimensions are used reducing the time for feature computation and matching. The indexing step is based on the sign of the Laplacian,which increases the matching speed and the robustness of the descriptor.

PCA-SIFT  and GLOH are variants of SIFT. PCA-SIFT descriptor is a vector of image gradients in x and y direction computed within the support region. The gradient region is sampled at 39x39 locations, therefore the vector is of dimension 3042. The dimension is reduced to 36 with PCA. Gradient location-orientation histogram (GLOH) is an extension of the SIFT descriptor designed to increase its robustness and distinctiveness. The SIFT descriptor is computed for a log-polar location grid with three bins in radial direction (the radius set to 6, 11, and 15) and 8 in angular direction, which results in 17 location bins. The central bin is not divided in angular directions. The gradient orientations are quantized in 16 bins resulting in 272 bin histogram. The size of this descriptor is reduced with PCA. The covariance matrix for PCA is estimated on image patches collected from various images. The 128 largest eigenvectors are used for description.

Wagner et al. developed two object recognition algorithms especially designed with the limitations of current mobile phones in mind. In contrast to the classic SIFT approach Wagner et al. use the FAST corner detector for feature detection. The algorithm also distinguishes between the off-line preparation phase where features are created at different scale levels and the on-line phase where features are only created at the current fixed scale level of the phone's camera image. In addition, features are created from a fixed patch size of 15x15 pixels and form a SIFT descriptor with only 36 dimensions. The approach has been further extended by integrating a Scalable Vocabulary Tree in the recognition pipeline. This allows the efficient recognition of a larger number of objects on mobile phones. The approach is mainly restricted by the amount of available RAM.


The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. In addition to these properties, they are highly distinctive, relatively easy to extract, allow for correct object identification with low probability of mismatch and are easy to match against a (large) database of local features. Object description by set of SIFT features is also robust to partial occlusion; as few as 3 SIFT features from an object are enough to compute its location and pose. Recognition can be performed in close-to-real time, at least for small databases and on modern computer hardware.


Scale-space extrema detection

This is the stage where the interest points, which are called keypoints in the SIFT framework, are detected. For this, the image is convolved with Gaussian filters at different scales, and then the difference of successive Gaussian-blurred images are taken. Keypoints are then taken as maxima/minima of the Difference of Gaussians (DoG) that occur at multiple scales. Specifically, a DoG image is given by is the convolution of the original image with the Gaussian blur at scale kσ, i.e., Hence a DoG image between scales kiσ and kjσ is just the difference of the Gaussian-blurred images at scales kiσ and kjσ. For scale-space extrema detection in the SIFT algorithm, the image is first convolved with Gaussian-blurs at different scales. The convolved images are grouped by octave (an octave corresponds to doubling the value of σ), and the value of ki is selected so that we obtain a fixed number of convolved images per octave. Then the Difference-of-Gaussian images are taken from adjacent Gaussian-blurred images per octave.

Once DoG images have been obtained, keypoints are identified as local minima/maxima of the DoG images across scales. This is done by comparing each pixel in the DoG images to its eight neighbors at the same scale and nine corresponding neighboring pixels in each of the neighboring scales. If the pixel value is the maximum or minimum among all compared pixels, it is selected as a candidate keypoint.

This keypoint detection step is a variation of one of the blob detection methods developed by Lindeberg by detecting scale-space extrema of the scale normalized Laplacian,that is detecting points that are local extrema with respect to both space and scale, in the discrete case by comparisons with the nearest 26 neighbours in a discretized scale-space volume. The difference of Gaussians operator can be seen as an approximation to the Laplacian, here expressed in a pyramid setting.

Keypoint localization After scale space extrema are detected (their location being shown in the uppermost image) the SIFT algorithm discards low contrast keypoints (remaining points are shown in the middle image) and then filters out those located on edges. Resulting set of keypoints is shown on last image.

Scale-space extrema detection produces too many keypoint candidates, some of which are unstable. The next step in the algorithm is to perform a detailed fit to the nearby data for accurate location, scale, and ratio of principal curvatures. This information allows points to be rejected that have low contrast (and are therefore sensitive to noise) or are poorly localized along an edge.

Interpolation of nearby data for accurate position

First, for each candidate keypoint, interpolation of nearby data is used to accurately determine its position. The initial approach was to just locate each keypoint at the location and scale of the candidate keypoint.The new approach calculates the interpolated location of the extremum, which substantially improves matching and stability.The interpolation is done using the quadratic Taylor expansion of the Difference-of-Gaussian scale-space function, with the candidate keypoint as the origin. This Taylor expansion is given by: where D and its derivatives are evaluated at the candidate keypoint and is the offset from this point. The location of the extremum, , is determined by taking the derivative of this function with respect to and setting it to zero. If the offset is larger than 0.5 in any dimension, then that's an indication that the extremum lies closer to another candidate keypoint. In this case, the candidate keypoint is changed and the interpolation performed instead about that point. Otherwise the offset is added to its candidate keypoint to get the interpolated estimate for the location of the extremum. A similar subpixel determation of the locations of scale-space extrema is performed in the real-time implementation based on hybrid pyramids developed by Lindeberg and his co-workers

Discarding low-contrast keypoints

To discard the keypoints with low contrast, the value of the second-order Taylor expansion is computed at the offset . If this value is less than 0.03, the candidate keypoint is discarded. Otherwise it is kept, with final location and scale σ, where is the original location of the keypoint at scale σ.

Eliminating edge responses

The DoG function will have strong responses along edges, even if the candidate keypoint is unstable to small amounts of noise. Therefore, in order to increase stability, we need to eliminate the keypoints that have poorly determined locations but have high edge responses.

For poorly defined peaks in the DoG function, the principal curvature across the edge would be much larger than the principal curvature along it. Finding these principal curvatures amounts to solving for the eigenvalues of the second-order Hessian matrix, H: The eigenvalues of H are proportional to the principal curvatures of D. It turns out that the ratio of the two eigenvalues, say α is the larger one, and β the smaller one, with ratio r = α / β, is sufficient for SIFT's purposes. The trace of H, i.e., Dxx + Dyy, gives us the sum of the two eigenvalues, while its determinant, i.e., , yields the product. The ratio can be shown to be equal to , which depends only on the ratio of the eigenvalues rather than their individual values. R is minimum when the eigenvalues are equal to each other. Therefore the higher the absolute difference between the two eigenvalues, which is equivalent to a higher absolute difference between the two principal curvatures of D, the higher the value of R. It follows that, for some threshold eigenvalue ratio rth, if R for a candidate keypoint is larger than , that keypoint is poorly localized and hence rejected. The new approach uses rth = 10.

This processing step for suppressing responses at edges is a transfer of a corresponding approach in the Harris operator for corner detection. The difference is that the measure for thresholding is computed from the Hessian matrix instead of a second-moment matrix (see structure tensor).

Orientation assignment

In this step, each keypoint is assigned one or more orientations based on local image gradient directions. This is the key step in achieving invariance to rotation as the keypoint descriptor can be represented relative to this orientation and therefore achieve invariance to image rotation.

First, the Gaussian-smoothed image at the keypoint's scale σ is taken so that all computations are performed in a scale-invariant manner. For an image sample at scale σ, the gradient magnitude, , and orientation, , are precomputed using pixel differences: The magnitude and direction calculations for the gradient are done for every pixel in a neighboring region around the keypoint in the Gaussian-blurred image L. An orientation histogram with 36 bins is formed, with each bin covering 10 degrees. Each sample in the neighboring window added to a histogram bin is weighted by its gradient magnitude and by a Gaussian-weighted circular window with a σ that is 1.5 times that of the scale of the keypoint. The peaks in this histogram correspond to dominant orientations. Once the histogram is filled, the orientations corresponding to the highest peak and local peaks that are within 80% of the highest peaks are assigned to the keypoint. In the case of multiple orientations being assigned, an additional keypoint is created having the same location and scale as the original keypoint for each additional orientation.

Keypoint descriptor

Previous steps found keypoint locations at particular scales and assigned orientations to them. This ensured invariance to image location, scale and rotation. Now we want to compute a descriptor vector for each keypoint such that the descriptor is highly distinctive and partially invariant to the remaining variations such as illumination, 3D viewpoint, etc. This step is performed on the image closest in scale to the keypoint's scale.

First a 4x4 array of histograms with 8 bins each is created. These histograms are computed from magnitude and orientation values of samples in a 16 x 16 region around the keypoint such that each histogram contains samples from a 4 x 4 subregion of the original neighborhood region. The magnitudes are further weighted by a Gaussian function with σ equal to 1.5 times the scale of the keypoint. The descriptor then becomes a vector of all the values of these histograms. Since there are 4 x 4 = 16 histograms each with 8 bins the vector has 128 elements. This vector is then normalized to unit length in order to enhance invariance to affine changes in illumination. To reduce the effects of non-linear illumination a threshold of 0.2 is applied and the vector is again normalized.

Although the dimension of the descriptor, i.e. 128, seems high descriptors with lower dimension than this don't perform as well across the range of matching tasks and the computational cost remains low due to the approximate BBF (see below) method used for finding the nearest-neighbor. Longer descriptors continue to do better but not by much and there is an additional danger of increased sensitivity to distortion and occlusion. It is also shown that feature matching accuracy is above 50% for viewpoint changes of up to 50 degrees. Therefore SIFT descriptors are invariant to minor affine changes. To test the distinctiveness of the SIFT descriptors, matching accuracy is also measured against varying number of keypoints in the testing database, and it is shown that matching accuracy decreases only very slightly for very large database sizes, thus indicating that SIFT features are highly distinctive.

Comparison of SIFT features with other local features

There has been an extensive study done on the performance evaluation of different local descriptors, including SIFT, using a range of detectors.The main results are summarized below:

The evaluations carried out suggests strongly that SIFT-based descriptors, which are region-based, are the most robust and distinctive, and are therefore best suited for feature matching. However, most recent feature descriptors such as SURF have not been evaluated in this study.

SURF has later been shown to have similar performance to SIFT, while at the same time being much faster.

Recently, a slight variation of the descriptor employing an irregular histogram grid has been proposed that significantly improves its performance. Instead of using a 4x4 grid of histogram bins, all bins extend to the center of the feature. This improves the descriptor's robustness to scale changes.



33rd Annual ACM SIGIR Conference
 SIGIR is the major international forum for the presentation of new research results and for the demonstration of new systems and techniques in the broad field of information retrieval (IR). The Conference and Program Chairs invite all those working in areas related to IR to submit original papers, posters, and proposals for tutorials, workshops, and demonstrations of systems. SIGIR 2010 welcomes contributions related to any aspect of IR theory and foundation, techniques, and applications. Relevant topics include, but are not limited to:



Follow this link.


Use: http://sigir2010.confmaster.net/.



Information on how to submit will be available by mid-December, 2009.

For other details, please see the conference web site: http://www.sigir2010.org/



New Version Of img(Rummager)
 
1. Evaluate the results using the new Mean Normalized Retrieval Order
2. BTDH descriptor bug fixed 
Stargate Studios Reel
 

Use pictures to search the web
 A picture is worth a thousand words.No need to type your search anymore. Just take a picture. Find out what businesses are nearby.Just point your phone at a store.

This is just the beginning - it's not quite perfect yet.Works well for some things, but not for all. Your pictures, your control.Turn on 'visual search history' to view or share your pictures at any time. Turn it off to discard them once the search is done.

Learn more in the Google Mobile Help Center

Go to Android market from your phone and search for "Google Goggles".

Available on phones that run Android 1.6+. Learn more 
Visual observation and analysis of animal and insect behavior
 A one day workshop to be held in conjunction with the

20th International Conference on Pattern Recognition (ICPR 2010)



and sponsored by the EU ChiRoPing project: Developing versatile and robust perception using sonar systems that integrate active sensing, morphology and behaviour.

There has been an enormous amount of research on analysis of video data of humans, but relatively little on visual analysis of other organisms. The goal of this workshop is to stimulate and bring together the current research in this area, and provide a forum for researchers to share expertise. As we want to make this more of a discussion workshop, we encourage work-in-progress presentations. Reviewing will be lightweight and only abstracts will be circulated to attendees.

The issues that the research will address include:

These problems can be applied to a variety of species at different sizes, such as fruit and house flies, crickets, cockroaches and other insects, farmed and wild fish, mice and rats, commercial farm animals such as poultry, cows and horses, and wildlife monitoring, etc. One aspect that they all have in common is video data.


Submit a 4 page extended abstract in English, in PDF, by email to email@example.com by April 1, 2010. The extended abstracts (PDF) should be anonymous. Include in your email the name(s) of the author(s), institutional affiliation, complete mailing address, international phone and fax numbers. The papers presented at the workshop will be published in the workshop notes, for distribution to the participants and linked to this page after the workshop.


Submission Deadline

Acceptance Notification

Revised Abstract Deadline


On-line registration of workshops will be available via the ICPR 2010 web page. It is not necessary to register for the full conference to attend the workshop.



Displaying Binary Images and Barcode
 Article from The Code Project

One of my friends was asking me how to display barcodes in ASP.NET. There is a great Open Source code written by Bart De Smet which can return a bar code image from a bar code string; you could see his article here. I have just added to it a small option of whether or not to display the code under the bar code. The library returns an Image. I thought, adding the display of the bar code from the bar code string value to RBMBinaryImage would be cool.


In the last version of RBMBinaryImage on CodeProject, some people pointed out that the control worked in Visual Studio perfectly but not well on some servers. I place here a solution for this problem posted by Louis, on of the control users on my site for the RBMBinaryImage, for users using the old version.

In the new version of the control, we can display a barcode as an image. Shown below is a screenshot: 
Connecting the Dots between Color Vision Experiments
 John J. McCann

Thomas Young, James Clerk Maxwell and Herman von Helmholtz describe human color as the trichromatic response to the quanta catch of three retinal receptors, at a pixel.  In the 204 years since Young’s trichromatic suggestion, there have been many hundreds, if not thousands, of vision experiments that demonstrate that two patches of retina stimulated by identical spectral radiances do not generate identical appearances.  The usual description of these experiments is they are illusions There is an alternative way to think about color vision, as succinctly put by Leonardo da Vinci: “Colors are most beautiful in the presence of their opposites”.  In this alternative construct, color illusions are simply color appearances synthesized from spatial comparisons.  For example, color constancy experiments show that objects have nearly constant appearance, despite significant changes in the spectral illumination.  Color appearance correlates with the spatial computation the object’s reflectance, using cone sensitivity functions.

This talk will discuss a wide variety of dramatic color experiments, including: constancy, adaptation, assimilation, Maximov’s shoe box, Land’s red and white photography, color-gamut mapping and color from rod/Lcone interactions.  All these experiments make a convincing argument that human vision differs from film and electronic sensors.  Vision works by making spatial comparisons.  We will discuss whether these experiments are in fact illusions, or the signature of vision’s underlying mechanisms.  As well, we will discuss whether many small modifications of trichromatic theory can account for illusions, or if this possibility is a delusion.





Scientific Peer Review, ca. 1945
 
YouTube Statistics
 Total videos uploaded as of March 17th 2008: 78.3 Million
Videos uploaded per day: over 200,000
A wildcard search on YouTube ("*") has given us the following "total results" (which approximates the total number of videos on YouTube)
January 28th 2008: 70 million
March 13th 2008: 77.4 million
March 17th 2008: 78.3 million
These numbers suggest that there are now at least150,000 and likely over 200,000 videos published everyday on YouTube.
The following statistics are derived from a *nearly* random sample of 232 videos on Wednesday, March 12th 2007.
Method: Every 2 hours, one of 8 researchers loaded the "Most Recent" videos on YouTube and analyzed the first 20 videos (the most recently added at that moment). This was done to eliminate the sampling bias of different times. Almost 20 videos were watched every 2 hours as they were uploaded to YouTube. 8 videos were blocked or removed before they could be viewed by the researchers, giving us the total of 232.
YouTube by Category: (Click here for a Tag Cloud Visualization)
Music: 19.8%
Entertainment: 19.0%
People & Blogs: 14.2%
Comedy: 13.4%
Sports: 6.9%
Education: 6.0%
Autos: 5.2%
Film: 4.7%
HowTo: 2.6%
News: 2.6%
Pets: 2.2%
Science: 2.2%
Travel: 1.3%
Most commonly used tags: (the following were the only tags used 4 or more times in the sample)
video, sexy, sex, music, rock, rap, funny, news, pop, dance, film, short, TV
Average Video Length: 2 minutes 46.17 seconds
Time it would take to view all of the material on YouTube (as of March 17th 2008): 412.3 years

Average Age of Uploader: 26.57
(note that this is not the average age of anybody who has ever uploaded a video, but the average age of active uploading)
Unambiguously User-Generated (amateur): 80.3%
Professional: 14.7%
Commercial Content Uploaded as percentage of Total Uploads: 4.7%
Vlogs Uploaded as percentage of Total Uploads: 4.7%
Percentage of videos that are probably in violation of copyright: 12%
Uploads by Country:
(Note: these statistics assume that upload traffic is consistent throughout a 24 hour period. For example the majority of YouTube videos are from the USA between 8pm - midnight CDT. If overall upload traffic is higher at this time than the early morning hours when USA percentage is low, then the USA may account for a higher percentage of total uploads. The reverse might also be true.)
USA: 34.5%
UK: 6.9%
Philippines: 3.9%
Turkey: 3.4%
Spain: 3.4%
Canada: 3.0%
Brazil: 3.0%
Germany: 2.6%
France: 2.6%
Mexico: 2.6%
Australia: 2.6%
Uploads by Language:
(same limitations as above for uploads by country apply here as well)
English: 48.1%
Spanish: 13.6%
Dutch: 3.9%
German: 2.9%
Portuguese: 2.9%




 GazoPa is a similar image search service on the web in open beta by Hitachi. Users can search images from the web based on user’s own photo, drawings, images found on the web and keywords. GazoPa enables users to search for a similar image from characteristics such as a color or a shape extracted from an image itself. There are abundant quantities of images on the web, however many of these simply cannot be described by keywords. Since GazoPa uses image features to search other similar images, a vast range of images can be retrieved from the web. GazoPa is a new visual search service that can navigate users to new territories on the web.




The 4th International Conference on Multimedia and Ubiquitous Engineering (MUE 2010)
 Recent advances in pervasive computers, networks, telecommunication, and information technology, along with the proliferation of multimedia-capable mobile devices, such as laptops, portable media players, personal digital assistants, and cellular telephones, have stimulated the development of intelligent and pervasive multimedia applications in a ubiquitous environment. The new multimedia standards (for example, MPEG-21) facilitate the seamless integration of multiple modalities into interoperable multimedia frameworks, transforming the way people work and interact with multimedia data. These key technologies and multimedia solutions interact and collaborate with each other in increasingly effective ways, contributing to the multimedia revolution and having a significant impact across a wide spectrum of consumer, business, healthcare, education, and governmental domains. This conference provides an opportunity for academic and industry professionals to discuss recent progress in the area of multimedia and ubiquitous environment including models and systems, new directions, novel applications associated with the utilization and acceptance of ubiquitous computing devices and systems.

The goals of this conference are to provide a complete coverage of the areas outlined and to bring together the researchers from academic and industry as well as practitioners to share ideas, challenges, and solutions relating to the multifaceted aspects of this field.

Download CFP(TXT, DOC, PDF)

The conference includes, but is not limited to, the areas listed below:


December 14, 2009 Workshop/Special session proposal

January 28, 2010 Paper submission deadline

February 28, 2010 Notification of acceptance

March 20, 2010 Camera-ready paper due

March 20, 2010 Presenting author registration due

June 28-30, 2010 Conference events

Paper Publication

All accepted papers will be included in the conference proceeding published by IEEE Press (IEEE eXpress Conference Publishing group) and will be included in the IEEE Xplorer. Outstanding papers will be invited for extension and publication in several journal special issues.



 Title: Image retrieval from the web using multiple features

Author(s): A. Vadivel, Shamik Sural, A.K. Majumdar


DOI: 10.1108/14684520911011061

Publisher: Emerald Group Publishing Limited


Purpose – The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths.
Design/methodology/approach – Image semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords.
Findings – The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques.
Research limitations/implications – The features of images used for capturing the semantics may not always describe the content.
Practical implications – The indexing mechanism for dynamically growing features is challenging while practically implementing the system.
Originality/value – A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind.



IEEE International Conference on Image Processing (ICIP 2010)
 The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP-2010, the seventeenth in the series that has been held annually since 1994, will bring together leading engineers and scientists in image processing from around the world. Research frontiers in fields ranging from traditional image processing applications to evolving multimedia and video technologies are regularly advanced by results first reported in ICIP technical sessions.

Topics of interest for submissions include, but are not limited to:

* Image/Video Coding and Transmission
* Image/Video Processing and Analysis
* Image Formation
* Image Scanning, Printing, Display and Color
* Image/Video Storage, Retrieval, and Authentication


Prospective authors are invited to submit extended summaries of not more than four (4) pages including results, figures and references. Papers will be accepted only by electronic submission through the conference web site. Prospective authors without web access should contact one of the Technical Program Chairs well before the submission deadline.

* Submission of full-length papers: January 25, 2010
* Notification of acceptance: April 26, 2010
* Submission of camera-ready papers: May 24, 2010

Proposals for Tutorials and Special Sessions

Tutorials will be held on Sunday, September 12, 2010. Proposals for tutorials must include a title, an outline of the tutorial and its motivation, contact information for the presenter(s), and a short description of the material to be covered. Proposals for tutorials should be submitted to the Tutorial Chair S.C. Chan (email@example.com) before January 11, 2010.

ICIP-2010 will include a number of special sessions. Proposals for special sessions must include a title, contact information for the session chair(s), and a list of authors who have been or will be contacted to present papers if the session is accepted. Proposals for special sessions should be submitted to the Special Sessions Co-Chair Oscar Au (firstname.lastname@example.org) before December 11, 2009.




Visual Studio Virtual Lab
 Build data-driven Web applications – fast

Virtual Labs are short, online, interactive, 30- to 90-minute demos. No download, installation or configuration required.

ASP.NET Dynamic Data provides a framework that lets you quickly build a functional data-driven application, based on a LINQ to SQL Server or Entity Framework data model. It also adds great flexibility and functionality to the DetailsView, FormView, GridView, and ListView controls with smart validation and the ability to easily change the display of these controls using templates. In this lab you'll learn how to create a data-driven Web application, add validation to the data model and customize ASP.NET Dynamic Data rendering, pages and fields.

Start the lab >

Introduction to the .NET Framework Client Profile

The .NET Framework Client Profile was created in response to feedback, from many customers, that indicated that a smaller framework was needed specifically for Client Applications. The Client Profile is a subset of assemblies already contained within .NET Framework 3.5 Service Pack 1. After completing this lab, you’ll be better able to target your client projects to the .NET Framework 3.5 Client Profile and verify which assembly references are not included in the Client Profile.


Develop an Outlook Add-In

Create applications that are more familiar to end-users with office-applications. Add-ins make it a snap to build your own features into Microsoft Office applications. Visual Studio Tools for Office, an integral technology of Visual Studio 2008, includes features that simplify add-in development. Once you’ve completed this lab, you’ll know how to bind Ribbon Events to WCF Service methods, create form regions and host a Windows Presentation Framework Custom Control.


Build outstanding end-user experiences with Windows Presentation Foundation (WPF)

WPF provides a unified framework for building applications and high-fidelity experiences in Windows that blend together application UI, documents and media content, while exploiting the full power of the local computing hardware. WPF was created to allow developers to more easily build the types of rich applications that were difficult to build with Windows Forms, the type that required a range of other technologies that were often hard to integrate.

Work through this lab, and you’ll learn how to use Visual Studio 2008 Designer to build a WPF client application, see how LINQ features can be used against a Microsoft SQL Server database, see how to access a WCF-based service after generating a Service Reference and put some of the new language features of C# to work.


Silverlight™ Monster Factory – Using a XAML Template

Microsoft Silverlight is a cross-browser, cross-platform plug-in for delivering the next generation of .NET-based media experiences and rich interactive applications for the Web. Silverlight offers a flexible programming model that supports AJAX, Visual Basic, C#, Python and Ruby, and integrates with existing Web applications. Silverlight supports fast, cost-effective delivery of high-quality video to major browsers running on the Mac OS or Windows.

In this lab, you will use Expression Design and Visual Studio 2008 to build a monster factory Web site, and also learn how to create XAML pictures and consume them using Silverlight and JavaScript from a Web site.


View all available Virtual Labs >


Visual Studio 2010 Beta
 Set your ideas free

Create what you can imagine, build on the strengths of your team, and open up new possibilities.

Simplicity through integration

A single integrated development environment that takes your skills further and adjusts to the way you work.

Quality tools help ensure quality results

Powerful testing tools with proactive project management features help you build the right app the right way.




13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2010)
 MICCAI 2010, the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, will be held from 20th to 24th September 2010 in Beijing, China. The venue for MICCAI 2010 is the China National Convention Center (CNCC). Located in the Beijing Olympic Green, the CNCC is right next to the Bird's Nest (China National Stadium), the Water Cube (National Aquatics Center) and the National Indoor Stadium.

Topics to be addressed in MICCAI 2010 include, but are not limited to:

Submission of papers
We invite electronic submissions for MICCAI 2010 (LNCS style, double blind review) of up to 8-page papers for oral or poster presentation. Papers will be reviewed by members of the programme review committee and assessed for quality and best means of presentation. Besides advances in methodology, we would also like to encourage submission of papers that demonstrate clinical relevance, clinical applications, and validation studies.

Proposals for Tutorials and Workshops
Tutorials will be held and will complement and enhance the scientific programme of MICCAI 2010. The purpose of the tutorials is to provide educational material for training new professionals in the field including students, engineers, clinicians and new researchers. The purpose of the workshops is to provide a comprehensive forum on topics that will not be fully explored during the main conference.


General Chair/Co-Chairs
Tianzi Jiang, Institute of Automation, Beijing, China (Chair)
Alan Colchester, University of Kent, UK
Jim Duncan, Yale University, USA

Programme Chair/Co-Chairs
Max Viergever, Utrecht University & UMC Utrecht, The Netherlands (Chair)
Nassir Navab, TU München, Germany
Josien Pluim, University Medical Center Utrecht, The Netherlands

Workshop Chair/Co-Chairs
Bram van Ginneken, Radboud University Nijmegen, The Netherlands (Chair)
Yong Fan, Institute of Automation, Beijing, China
Polina Golland, Massachusetts Institute of Technology, USA
Tim Salcudean, University of British Columbia, Canada

Tutorial Chair/Co-Chairs
Dinggang Shen, University of North Carolina, USA (Chair)
Alejandro Frangi, Universitat Pompeu Fabra, Barcelona, Spain
Gabor Szekely, ETH Zurich, Switzerland



Show me the pictures: better format for image results
 I love when I get images back in my Google search results. There's no better way to quickly understand the difference between an ocelot and a clouded leopard. But sometimes I want to see more images to really make sure I've identified the right jungle cat.
Over the next twenty-four hours we're rolling out a new format for image universal results. When we're confident that we have great image results, we'll now show a larger image and additional smaller images alongside. With this new layout we're able to show you more pictures than before, so you have more to choose from. As always, you can click on an image to see it full size in the original webpage. 

An adaptable image retrieval system with relevance feedback using kernel machines and selective sampling
 This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided.




ACM Multimedia 2010
 ACM Multimedia 2010 is the worldwide premier multimedia conference and a key event to display scientific achievements and innovative industrial products. ACM Multimedia 2010 offers to scientists and practitioners in the area of Multimedia plenary scientific and technical sessions – with keynote speeches, oral, poster and video presentations, technical demonstrations and exhibits -, opportunities for deepenings – organized as discussion rooms, symposiums and tutorials and panels with worldwide recognized scientists and opinion leaders -, and competitions of research teams on relevant and challenging questions about the industry’s two-five years horizon for multimedia. As a companion event, the Interactive Art program will provide the opportunity of interaction between artists and computer scientists and investigation on the application of multimedia technologies to art and cultural heritage. Workshops associated to ACM Multimedia 2010 will provide in-focus forums of discussion on some of the most relevant and timely scientific topics in the field.

In more detail the complete program of ACM Multimedia 2010 will include:

Scientific presentations

Interactive Art program


In-depth courses and discussions

Demos and exhibits




Limitations of Content-based Image Retrieval
 Theo Pavlidis
© Copyright 2008



This is a viewpoint paper where I discuss my impressions from the current state of the art and then I express opinions on what might be fruitful approaches. I find the current results in CBIR very limited in spite of over 20 years of research efforts. Certainly, I am not the only one who thinks that way, the lead editorial of a recent special issue of the IEEE Proceedings on multimedia retrieval was titled "The Holy Grail of Multimedia Information Retrieval: So Close or Yet So Far Away?"

I offer certain reasons for this state of affairs, especially for the discrepancy between high quality results shown in papers and poorer results in practice. The main reason seems to be that the lessons about feature selection and the "curse of dimensionality" in pattern recognition have been ignored in CBIR. Because there is little connection between pixel statistics and the human interpretation of an image (the "semantic gap") the use of large number of generic features makes highly likely that results will not be scalable, i.e. they will not hold on collections of images other than the ones used during the development of the method. In other words, the transformation from images to features (or other descriptors) is many-to-one and when the data set is relatively small, there are no collisions. But as the size of the set increases unrelated images are likely to be mapped into the same features.

I propose that generic CBIR will have to wait both for algorithmic advances in image understanding and advances in computer hardware. In the meantime I suggest that efforts should be focused on retrieval of images in specific applications where it is feasible to derive semantically meaningful features.

There are two appendices with examples of image retrieval. One presents the results obtained from some on line systems and the other presents some experiments I conducted to demonstrate how a method that yields impressive results in the author(s) paper gives poor results in independent tests. I have included a set of images that provide a challenge to the current CBIR methodologies. Two other appendices illustrate the severe limitations of color and edge strength histograms respectively for image characterization.




Semantic Annotation of Medical Images
 Sascha Seifert, Michael Kelm, Manuel Möller, Saikat Mukherjee, Alexander Cavallaro, Martin Huber, Dorin Comaniciu: “Semantic Annotation of Medical Images”, to appear in Proc. of SPIE Medical Imaging, San Diego, 2010.

Abstract: Diagnosis and treatment planning for patients can be signi cantly improved by comparing with clinical images of other patients with similar anatomical and pathological characteristics. This requires the images to be annotated using common vocabulary from clinical ontologies. Current approaches to such annotation are typically manual, consuming extensive clinician time, and cannot be scaled to large amounts of imaging data in hospitals. On the other hand, automated image analysis while being very scalable do not leverage standardized semantics and thus cannot be used across specific applications. In our work, we describe an automated and context-sensitive work based on an image parsing system complemented by an ontology-based context-sensitive annotation tool. An unique characteristic of our framework is that it brings together the diverse paradigms of machine learning based image analysis and ontology based modeling for accurate and scalable semantic image annotation.

This entry was posted on Wednesday, October 21st, 2009 at 5:27 pm and is filed under 2010, Co-Author, Conference, DFKI, English, MEDICO, Semantic Image Retrieval. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.



A Multimodal Dialogue Mashup for Medical Image Semantics
 Daniel Sonntag, Manuel Möller: “A Multimodal Dialogue Mashup for Medical Image
Semantics”, to appear in Proceedings of the International Conference on Intelligent User Interfaces (IUI 2010), Hong Kong, China, 7.-10. Februar 2010

Abstract: This paper presents a multimodal dialogue mashup where different users are involved in the use of different user interfaces for the annotation and retrieval of medical images. Our solution is a mashup that integrates a multimodal interface for speech-based annotation of medical images and dialogue-based image retrieval with a semantic image annotation tool for manual annotations on a desktop computer. A remote RDF repository connects the annotation and querying task into a common framework and serves as the semantic backend system for the advanced multimodal dialogue a radiologist can use.

This entry was posted on Monday, November 30th, 2009 at 10:24 am and is filed under 2010, Co-Author, Conference, DFKI, English, MEDICO, User Interfaces. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.