NDLI: Data driven multi-index hashing

Content Provider	IEEE Xplore Digital Library
Author	Ji Wan Sheng Tang Yongdong Zhang Lei Huang Jintao Li
Copyright Year	2013
Description	Author affiliation: Adv. Comput. Res. Lab., Inst. of Comput. Technol., Beijing, China (Ji Wan; Sheng Tang; Yongdong Zhang; Lei Huang; Jintao Li)
Abstract	Binary representation for large scale nearest neighbor search received more and more concern recently. Although binary codes can be directly used as indices of the hash tables, correlations between the bits may lead to non-uniform codes distribution and reduce the performance of the hash table. In this paper, we propose a data driven multi-index hashing method for exact nearest neighbor search in Hamming space. By exploring the statistics properties of the dataset, we can separate the correlated bits into different segments during the process of building multiple hash tables, and thus make binary codes distributed as uniformly as possible in each hash table. Experiments conducted on a huge amount of binary codes extracted from the UK Bench dataset show that our method can achieve significant acceleration in searching speed for large scale dataset.
Starting Page	2670
Ending Page	2673
File Size	175562
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738550
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Nearest neighbor search Binary codes Indexing
Content Type	Text
Resource Type	Article

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