Adaptive bit allocation hashing for approximate nearest neighbor search

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	Adaptive bit allocation hashing for approximate nearest neighbor search
	Guo QZ(郭勤振); Zhi Zeng; Shuwu Zhang
发表期刊	Neurocomputing
	2015
卷号	151 期号:151 页码:719-728
摘要	Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attention. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two mainsteps: projection and thresholding. One problem with these methods is that every dimension of the projected data is regarded as of same importance and encoded by one bit, which may result in ineffective codes. In this paper, we introduce an adaptive bit allocation hashing(ABAH) method to encode data for ANN search. The basic idea is, according to the dispersions of all the dimensions after projection we use different numbers of bits to encode them. In our method, more bits will be adaptively allocated to encode dimensions with larger dispersion while fewer bits for dimensions with smaller dispersion. This novel bit allocation scheme makes our hashing method effectively preserve the neighborhood structure in the original dataspace. Extensive experiments show that the proposed ABAH significantly outperforms other state-of-the-art methods for ANN search task..
关键词	Adaptive Bit Allocation Image Hashing
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11590
专题	数字内容技术与服务研究中心_版权智能与文化计算
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Guo QZ,Zhi Zeng,Shuwu Zhang. Adaptive bit allocation hashing for approximate nearest neighbor search[J]. Neurocomputing,2015,151(151):719-728.
APA	Guo QZ,Zhi Zeng,&Shuwu Zhang.(2015).Adaptive bit allocation hashing for approximate nearest neighbor search.Neurocomputing,151(151),719-728.
MLA	Guo QZ,et al."Adaptive bit allocation hashing for approximate nearest neighbor search".Neurocomputing 151.151(2015):719-728.

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