Image patches can be factorized into `shapelets' that describe segmentation
patterns called structural elements (stels), and palettes that describe
how to paint the shapelets. We introduce local palettes for patches,
global palettes for entire images and universal palettes for image
collections. Using a learned shapelet library, patches from a test
image can be analyzed using a variational technique to produce an
image descriptor that represents local shapes and colors separately.
We show that the shapelet model performs better than SIFT, Gist and
the standard stel method on Caltech28 and is very competitive with
other methods on Caltech101.
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