Uncorrelated Component Analysis-Based Hashing

The approximate nearest neighbor (ANN) search problem is important in applications such as information retrieval. Several hashing-based search methods that provide effective solutions to the ANN search problem have been proposed. However, most of these focus on similarity preservation and coding error minimization, and pay little attention to optimizing the precision-recall curve or receiver operating characteristic curve. In this paper, we propose a novel projection-based hashing method that attempts to maximize precision and recall. We first introduce an uncorrelated component analysis (UCA) transformation by examining precision and recall, and then propose a UCA-based hashing method. The proposed method is evaluated with a variety of data sets. The results show that UCA-based hashing outperforms state-of-the-art methods, and has computationally efficient training and encoding processes.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-08
Language
English
Keywords

NEAREST-NEIGHBOR SEARCH; PRODUCT QUANTIZATION; CODES

Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.26, no.8, pp.3759 - 3774

ISSN
1057-7149
DOI
10.1109/TIP.2017.2695099
URI
http://hdl.handle.net/10203/224853
Appears in Collection
EE-Journal Papers(저널논문)
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