Answers.Body: <p>I am assuming each image contains a single object.</p>
  
  <p>It is possible, however, it is not as easy as you might think. Firstly, you need extract as many features as possible: original image, <a href="https://en.wikipedia.org/wiki/Local_binary_patterns" rel="nofollow">LBP</a>, <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform" rel="nofollow">SIFT</a>, <a href="https://en.wikipedia.org/wiki/Image_moment#Moment_invariants" rel="nofollow">moments</a>, contour descriptors to name a few. Than concatenate these features into a single feature vector. After this step, use clustering. You will need a lot of samples to compensate for the number of features. After clustering, use a correlation method to find which features are related to each cluster.</p>
  
  <p>If you need features to classify within a cluster, you could do a second clustering with full set of features and apply the same method. The features that are selected for a cluster will not be suitable to classify within the cluster.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-04T06:25:20.620Z
Answers.LastActivityDate: 2016-08-04T06:25:20.620Z
Answers.UserDisplayName: Cem Kalyoncu
Body: <p>Is it possible for <em>unsupervised learning</em> to learn about high-level, class-specific features given only unlabelled images? For example detecting human or animal faces? If so, how?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-04T00:25:17.443Z
Comments.Text: This question has been flagged as too broad. You might want to edit the title to ask only if it's possible or useful, not how to do it.
Comments.UserDisplayName: Ben N
Tags: <image-recognition><unsupervised-learning>
Title: Is it possible for 'unsupervised learning' model to recognize features on unlabelled images?
UserDisplayName: kenorb
ThreadText: # Is it possible for 'unsupervised learning' model to recognize features on unlabelled images?
  
  (Asked by: username_0 on 2016-08-03)
  
  Is it possible for *unsupervised learning* to learn about high-level, class-specific features given only unlabelled images? For example detecting human or animal faces? If so, how?
  
  **Tags:** image-recognition, unsupervised-learning
  
  > **Comments:**
  > **username_1**: This question has been flagged as too broad. You might want to edit the title to ask only if it's possible or useful, not how to do it.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2016-08-04. Score: 1 | Accepted answer
  
  I am assuming each image contains a single object.
  
  It is possible, however, it is not as easy as you might think. Firstly, you need extract as many features as possible: original image, [LBP](https://en.wikipedia.org/wiki/Local_binary_patterns), [SIFT](https://en.wikipedia.org/wiki/Scale-invariant_feature_transform), [moments](https://en.wikipedia.org/wiki/Image_moment#Moment_invariants), contour descriptors to name a few. Than concatenate these features into a single feature vector. After this step, use clustering. You will need a lot of samples to compensate for the number of features. After clustering, use a correlation method to find which features are related to each cluster.
  
  If you need features to classify within a cluster, you could do a second clustering with full set of features and apply the same method. The features that are selected for a cluster will not be suitable to classify within the cluster.
  
  
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