Efficient and Flexible Bitmap Indexing for Complex Similarity Queries

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Abstract

In this paper, we propose a novel indexing method for complex similarity queries in high-dimensional image and video databases. In order to provide the indexing method with the flexibility in dealing with multiple features and multiple query objects, we treat every dimension independently. The efficiency of our method is realized by a specialized bitmap indexing that represents all objects in a database as a set of bitmaps. The percentage of data accessed in our indexing method is inversely proportional to the overall dimensionality, and thus the performance deterioration with the increasing dimensionality does not occur. To demonstrate the efficacy of our method we conducted extensive experiments and compared the performance with the linear scan by using real image and video datasets, and obtained a remarkable speed-up over the linear scan.