NDLI: Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search

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Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search

Content Provider	ACM Digital Library
Author	Lang, Bo Zhang, Danchen Liu, Xianglong
Abstract	Hashing for nearest neighbor search has attracted great attentions in the past years. Many hashing methods have been successfully applied in real-world applications like the mobile product search. The performance of these applications usually highly relies on the quality of hash bits. However, it still lacks of a general method that can provide good hash bits for different scenarios. In this paper, we propose a novel method that can select compact, independent and informative hash bits using the Markov Process. Our method can serve as a unified framework compatible with different hashing methods. We design two algorithms, BS-CMP and BS-DMP, and formulate the selection problem as the subgraph discovery on a graph. Experiments are conducted for two important selection scenarios when applying hash techniques, i.e., hashing using different hashing algorithms and hashing with multiple features. The result indicates that our proposed bit selection approaches outperform naive selection methods significantly under aforementioned two scenarios.
Starting Page	205
Ending Page	208
Page Count	4
File Format	PDF
ISBN	9781450321068
DOI	10.1145/2536853.2536934
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-12-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Markov process Locality sensitive hashing Nearest neighbor search Hash bit selection
Content Type	Text
Resource Type	Article

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