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Suppose you have a data set composed of n images as training examples. You run clustering on each image ( initializing 3 clusters per image) and learn the centers. Is it ok to then take the cluster centers themselves as features for a supervised learning algorithm and thus have a vocabulary for each image that way or is it inconsistent ? Are there are other more consistent measures that can be used ?

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closed as not a real question by Jeffε, Tsuyoshi Ito, Lev Reyzin, David Eppstein, Suresh Venkat Jan 30 '13 at 9:24

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    $\begingroup$ Voting to close as "not a real question". I assume an "image" is a pixelmap, but in this context, what's a "feature"? What's a "center"? What's a "vocabulary"? What is "inconsistent"? Why three clusters, and not 2 or 17? What problem are you actually trying to solve? $\endgroup$ – Jeffε Jan 29 '13 at 15:35
  • $\begingroup$ @JɛffE I think valentin are talking about the Bag of Words algorithm. $\endgroup$ – Shawn Xie Feb 1 '13 at 2:03
  • $\begingroup$ Yes indeed I was having the Bag-of-Words Algorithm on my mind. "Mea culpa" for not being explicit enough. Thanks. $\endgroup$ – valentin Feb 1 '13 at 17:13
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I'd say you can. "Coordinates of point of importance inside image" qualifies as a feature so you can use it in supervised learning. If clustering is a way to discover that coordinate you can use that.

A practical example: say you want to use "x-coordinate of left eye" as a feature in a classification problem. It does not matter how you get the object's values for that feature, as long as the values are accurate. If you have an algorithm (e.g. a clustering algorithm) which can compute the values you can use it.

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