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2013-09-19T17:16:59.527
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This is a tough problem and therefore there are many approaches. On common method (although complicated) is taken an input image, superpixelate the image and compute descriptors (such as <a href="http://en.wikipedia.org/wiki/Scale-invariant_feature_transform" rel="noreferrer">SIFT</a> of <a href="http://en.wikipedia.org/wiki/SURF" rel="noreferrer">SURF</a>) of those superpixels building a bag-of-word representation by accumulating histograms per superpixel, this operation extracts the key information from a bunch of pixels reducing dimensionality. Then a <a href="http://en.wikipedia.org/wiki/Conditional_random_field" rel="noreferrer">Conditional Random Field</a> algorithm searches for relationships between superpixels in the image and classifies the group of pixels inside a known category. For pixelating images <a href="http://scikit-image.org/" rel="noreferrer"><code>scikit-image</code></a> package implements SLIC algorithm <a href="http://scikit-image.org/docs/dev/api/skimage.segmentation.html?highlight=slic#skimage.segmentation.slic" rel="noreferrer"><code>segmentation.slic</code></a>, and for the CRF you should take a look to <a href="http://pystruct.github.io/" rel="noreferrer"><code>PyStruct</code></a> package. SURF and SIFT can be calculated using OpenCV. <img src="https://i.stack.imgur.com/JiEXR.jpg" alt="enter image description here"> Another simple version would be computing descriptors of a given image (SIFT, SURF, borders, histogram etc) and use them as inputs in a classifier algorithm, you might want start from here, maybe <a href="http://scikit-learn.org/" rel="noreferrer"><code>scikit-learn.org</code></a> is the easiest and most powerful package for doing this.
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1. POWhat are good features for classifying photos of clothing?
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1. USjabaldonedo
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