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Color histograms are widely used for content-based image retrieval. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very different appearances can have similar histograms. We describe a technique for comparing images called histogram refinement, which imposes additional constraints on histogram based matching. Histogram refinement splits the pixels in a given bucket into several classes, based upon some local property. Within a given bucket, only pixels in the same class are compared. We describe a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence. CCV's can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried using CCV's in under 2 seconds. We demonstrate that histogram refinement can be used to distinguish images whose color histograms are indistinguishable.
A paper entitled Histogram Refinement for Content-Based Image Retrieval will appear in the Workshop on the Applications of Computer Vision. This paper is available in PDF format here. Free PDF readers can be obtained for many platforms from Adobe.
A slightly older paper entitled Comparing Images Using Color Coherence Vectors will appear in the 1996 ACM Conference on Multimedia, and is available in PDF format here.