Answers.Body: <p>You could run your images through a pretrained CNN for feature extraction, then run an unsupervised algorithm to see if there are any obvious groups of &quot;dogs&quot; and &quot;cups&quot;.</p>
  <p>Don't know how you would procced with localization though. I'm afraid you'll have to hand-label your images via some <a href="https://dida.do/blog/the-best-labeling-tools-for-computer-vision" rel="nofollow noreferrer">tool</a>. Maybe there are some pretrained models for localization you could use.</p>
  
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Answers.CreationDate: 2022-05-02T08:42:11.930Z
Answers.Text: That is kinda what I hoped to avoid, to have some pretrained CNNs, since it seems (to me) to restricting with respect to the types of objects I can detect. I think it would be interesting in general to be able to get for example the outlines of an arbitrary shape. With the Hamilton-Jacobi Level-Set approach I am able to do so to some extent but it is quite slow, so I cannot transfer it to videos. The suggestion of @nbro with k-means at least sped up this approach. However still not nearly as fast as a pretrained CNN. Anyway still thanks for your suggestion !
Answers.UserDisplayName: Astraeus
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Answers.CreationDate: 2022-04-29T10:11:56.613Z
Answers.LastActivityDate: 2022-04-29T10:11:56.613Z
Answers.UserDisplayName: Adrian Evensen
Body: <p>Is there way to segment an image without labeling/classification, as well as supervised learning?</p>
  <p>For an illustrative example, if one considers an image with a dog and a cup (we don't particularly care what is in the picture), what are viable approaches (if there exist any) to segment these objects in order to perform localization afterwards?</p>
  <p>How can one detect arbitrary objects in an image?</p>
  <p>So far, I tried a level set approach using a Hamilton-Jacobi Equation to model the energy within the picture. However, this is extremely slow (since iterative) and highly depends on the preprocessing of the image to detect anything.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-04-27T10:22:35.963Z
Comments.Text: Are you looking for an unsupervised learning technique to find the objects? You could cluster the image (e.g. with k-means) - if you're lucky, the clusters will be the objects. Maybe there's some specific model/algorithm that was specifically created to do your task.
Comments.UserDisplayName: nbro
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Comments.CreationDate: 2022-04-27T11:03:18.697Z
Comments.Text: Yes some kind of unsupervised approach where no knowledge of the object is a priori known. Thanks for the idea I will have a look into clustering. Ultimately, my goal is to compute the positions of these objects robustly.
  
  (And maybe extend it to tracking using MPC, to extend the research)
Comments.UserDisplayName: Astraeus
Tags: <machine-learning><reference-request><object-detection><image-segmentation><algorithm-request>
Title: Is there way to segment an image without labeling/classification, as well as supervised learning?
UserDisplayName: Astraeus
ThreadText: # Is there way to segment an image without labeling/classification, as well as supervised learning?
  
  (Asked by: username_0 on 2022-04-26)
  
  Is there way to segment an image without labeling/classification, as well as supervised learning?
  
  For an illustrative example, if one considers an image with a dog and a cup (we don't particularly care what is in the picture), what are viable approaches (if there exist any) to segment these objects in order to perform localization afterwards?
  
  How can one detect arbitrary objects in an image?
  
  So far, I tried a level set approach using a Hamilton-Jacobi Equation to model the energy within the picture. However, this is extremely slow (since iterative) and highly depends on the preprocessing of the image to detect anything.
  
  **Tags:** machine-learning, reference-request, object-detection, image-segmentation, algorithm-request
  > **Comments:**
  > **username_1**: Are you looking for an unsupervised learning technique to find the objects? You could cluster the image (e.g. with k-means) - if you're lucky, the clusters will be the objects. Maybe there's some specific model/algorithm that was specifically created to do your task.  
  > **username_0**: Yes some kind of unsupervised approach where no knowledge of the object is a priori known. Thanks for the idea I will have a look into clustering. Ultimately, my goal is to compute the positions of these objects robustly.  (And maybe extend it to tracking using MPC, to extend the research)  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-04-29. Score: 1
  
  You could run your images through a pretrained CNN for feature extraction, then run an unsupervised algorithm to see if there are any obvious groups of "dogs" and "cups".
  
  Don't know how you would procced with localization though. I'm afraid you'll have to hand-label your images via some [tool](https://dida.do/blog/the-best-labeling-tools-for-computer-vision). Maybe there are some pretrained models for localization you could use.
  
  > **Comments:**
  > **username_0**: That is kinda what I hoped to avoid, to have some pretrained CNNs, since it seems (to me) to restricting with respect to the types of objects I can detect. I think it would be interesting in general to be able to get for example the outlines of an arbitrary shape. With the Hamilton-Jacobi Level-Set approach I am able to do so to some extent but it is quite slow, so I cannot transfer it to videos. The suggestion of @nbro with k-means at least sped up this approach. However still not nearly as fast as a pretrained CNN. Anyway still thanks for your suggestion !  
  
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