Greg Pass and Ramin Zabih,
Histogram Refinement for Content-Based Image Retrieval,
In IEEE Workshop on Applications of Computer Vision,
Sarasota, Florida, USA, pp. 96-102, December 1996.
Greg Pass and Ramin Zabih,
Histogram Refinement for Content-Based Image Retrieval,
In IEEE Workshop on Applications of Computer Vision,
Sarasota, Florida, USA, pp. 96-102, December 1996.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.