Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 28, Issue 11, Pages 2884-2894Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2597834
Keywords
Approximate nearest neighbor search; binary codes; large-scale retrieval; vector quantization
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In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.
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