Answers.Body: <p>An image of a natural domain refers to any kind of image that has some variance in its structure, so it does not always present the same peculiarities. </p>
  
  <p>For example, in a digits classification task, you can train your network on the MNIST dataset, that is a simple black &amp; white set of images that are always well defined: the digit is in darker pixels, and the background is always white, they just have different shapes. </p>
  
  <p>For the same task but in the natural image domain, the digits may vary in color, noise, and even position inside of a single image. As you may imagine this does not apply to digits only, but it includes any image that was not specifically created or modified to be fed into a neural network.</p>
  
  <p>So, to put it simply, a neural network trained for natural images is able to perform classification in any real-life situation or scenario, without the need to edit the image to adapt it to what the network was trained for (for example, changing the color or cropping so the object you want to recognize is exactly in the center).</p>
  
  <p><a href="https://stats.stackexchange.com/q/25737/82135">Here</a> a similar question that may fill some doubts you may have.</p>
  
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Answers.CreationDate: 2020-04-10T14:46:40.610Z
Answers.Text: I think that this sentence "a neural network trained for natural images is able to perform classification in any real-life situation or scenario" is misleading. What you wanted to say is that you don't have to pre-process the inputs before feeding them into the network. AFAIK, no neural network is able to perform classification in ANY real-life situation or scenario.
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Answers.Text: Yes, i gave for granted that a neural network can only classify objects that it was trained for
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Body: <p>I see some papers use the term "natural image domain". I googled that but didn't find any explanation of it.</p>
  
  <p>I guess I understand the normal meaning of "natural image", such as the image people take by phones. The images in ImageNet database are all natural images. </p>
  
  <p>Is "natural image domain" a subfield of computer vision?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-10T03:37:10.670Z
Comments.Text: Hi and welcome to AI SE! Can you please link to the _specific_ papers that use the term "natural image domain"?
Comments.UserDisplayName: nbro
Tags: <deep-learning><computer-vision><terminology>
Title: What is "natural image domain"?
UserDisplayName: Piete3r
ThreadText: # What is "natural image domain"?
  
  (Asked by: username_0 on 2020-04-10)
  
  I see some papers use the term "natural image domain". I googled that but didn't find any explanation of it.
  
  I guess I understand the normal meaning of "natural image", such as the image people take by phones. The images in ImageNet database are all natural images.
  
  Is "natural image domain" a subfield of computer vision?
  
  **Tags:** deep-learning, computer-vision, terminology
  
  > **Comments:**
  > **username_1**: Hi and welcome to AI SE! Can you please link to the _specific_ papers that use the term "natural image domain"?  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2020-04-10. Score: 0 | Accepted answer
  
  An image of a natural domain refers to any kind of image that has some variance in its structure, so it does not always present the same peculiarities.
  
  For example, in a digits classification task, you can train your network on the MNIST dataset, that is a simple black & white set of images that are always well defined: the digit is in darker pixels, and the background is always white, they just have different shapes.
  
  For the same task but in the natural image domain, the digits may vary in color, noise, and even position inside of a single image. As you may imagine this does not apply to digits only, but it includes any image that was not specifically created or modified to be fed into a neural network.
  
  So, to put it simply, a neural network trained for natural images is able to perform classification in any real-life situation or scenario, without the need to edit the image to adapt it to what the network was trained for (for example, changing the color or cropping so the object you want to recognize is exactly in the center).
  
  [Here](https://stats.stackexchange.com/q/25737/82135) a similar question that may fill some doubts you may have.
  
  > **Comments:**
  > **username_1**: I think that this sentence "a neural network trained for natural images is able to perform classification in any real-life situation or scenario" is misleading. What you wanted to say is that you don't have to pre-process the inputs before feeding them into the network. AFAIK, no neural network is able to perform classification in ANY real-life situation or scenario.  
  > **username_2**: Yes, i gave for granted that a neural network can only classify objects that it was trained for  
  
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