Spatial color indexing and applications

We define a new image feature called the color correlogram and use it for i mage indexing and comparison. This feature distills the spatial correlation of colors and when computed efficiently, turns out to be both effective an d inexpensive for content-based image retrieval. The correlogram is robust in tolerating large changes in appearance and shape caused by changes in vi ewing position, camera zoom, etc. Experimental evidence shows that this new feature outperforms not only the traditional color histogram method but al so the recently proposed histogram refinement methods for image indexing/re trieval. We also provide a technique to cut down the storage requirement of the correlogram so that it is the same as that of histograms, with only ne gligible performance penalty compared to the original correlogram. We also suggest the use of color correlogram as a generic indexing tool to tackle various problems arising from image retrieval and video browsing. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental resul ts again suggest that the color correlogram is more effective than the hist ogram for these applications, with insignificant additional storage or proc essing cost.