Abstract
Similarity search, or finding approximate nearest neighbors, is becoming an increasingly important tool to find the closest matches for a given query object in large scale database. Recently, learning hashing-based methods have attracted considerable attention due to their computational and memory efficiency. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. In this paper, we propose a novel algorithm named Approximate Bit-Vector (ABV) for hashing-based similarity search. ABV algorithm map data points into Hamming space and integrate with hash functions for fast similarity or k-NN search. Extensive experimental results over real large-scale datasets demonstrate the superiority of the proposed approach.
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Acknowledgments
This work was supported by the Science and Technology Plan Projects of Jilin city (No. 201464059), by the Ph.D. Scientific Research Start-up Capital Project of Northeast Dianli University (No. BSJXM-201319), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2013R1A2A2A01068923).
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Wang, L., Zhou, T.H., Liu, Z.H., Qu, Z.Y., Ryu, K.H. (2015). Approximate Bit-Vector Algorithms for Hashing-Based Similarity Searches. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_61
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DOI: https://doi.org/10.1007/978-3-319-22180-9_61
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