An Extendible Hash for Multi-Precision Similarity Querying of Image Databases

摘要

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.