Combining supervised learning with color correlograms for content-based image retrieval
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We suggest the use of the color correlogram as a generic indexing tool to tackle various computer vision problems. Correlograms were shown to be very effective for content-basedimage retrieval [4]. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results suggest that the color correlogram is much more effective than the histogram for these applications, with insignificant additional computational, storage, or processing cost. We also provide a technique to cut down the storage requirement of correlograms so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.