We propose multi-precision similarity matching where the image is divided into a number of 
sub-blocks, each with its associated color histogram. We present experimental results 
showing that the spatial distribution information recorded by multi-precision color 
histograms helps to make similarity matching more precise. We also show that sub-image 
queries are much better supported with multi-precision color histograms. To minimize the 
overhead, we employ a filtering scheme based on the 3-dimensional average color vectors. 
We provide a formal result proving that filtering with multi-precision color histograms is 
complete. Finally, we develop a novel extendible hashing structure for indexing the average 
color vectors. We give experimental results showing that the proposed structure 
significantly outperforms the SR-tree.