An approximate query processing approach for a content-based image retrieval method based on probabilistic relaxation labeling is proposed. The novelty lies in the inclusion of a filtering mechanism based on a quasi lower bound on distance in the vector space that effectively spares the matching between the query and a number of database images from going through the expensive step of iterative updating the labeling probabilities. This resembles the two-step filter-and-refine query processing approach that has been applied to k-nearest neighbor (k-NN) retrieval in database research. It is confirmed by experiments that the proposed approach consistently returns a close "approximation" of the accurate result, in the sense of the first k' in the top k output of a k-NN search, while simultaneously reduces the amount of processing required. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Kwan, P. W. H., Toraichi, K., Kitagawa, H., & Kameyama, K. (2003). Approximate query processing for a content-based image retrieval method. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2736, 517–526. https://doi.org/10.1007/978-3-540-45227-0_51
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