期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 9, 页码 2827-2840出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2421443
关键词
Hashing; nearest neighbor search; image retrieval; binary codes; neighborhood discriminant information; maximum entropy principle
资金
- 973 Program [2014CB347600]
- National Natural Science Foundation of China [61402228]
- Natural Science Foundation of Jiangsu Province [BK2012033, BK20140058]
- Program for New Century Excellent Talents in University [NCET-12-0632]
- Open Projects Program of National Laboratory of Pattern Recognition
With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i. e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.
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