Journal
PATTERN RECOGNITION
Volume 78, Issue -, Pages 79-90Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.01.007
Keywords
Supervised hash learning; Discrete hash learning; Discrete hash codes; Discriminant information; Robust similarity metric
Funding
- National Science Foundation of China [61601235, 61573248, 61773328, 61732011]
- Natural Science Foundation of Jiangsu Province of China [BK20170768, BK20160972]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [17KJB520019, 16KJB520031]
- Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology [2243141601019]
- Shenzhen Municipal Science and Technology Innovation Council [JCYJ20170302153434048]
- Natural Science Foundation of Guangdong Province [2017A030313367]
- Research Grant of the Hong Kong Polytechnic University [4-ZZDR]
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Most existing hashing methods usually focus on constructing hash function only, rather than learning discrete hash codes directly. Therefore the learned hash function in this way may result in the hash function which can-not achieve ideal discrete hash codes. To make the learned hash function for achieving ideal approximated discrete hash codes, in this paper, we proposed a novel supervised discrete discriminant hashing learning method, which can learn discrete hashing codes and hashing function simultaneously. To make the learned discrete hash codes to be optimal for classification, the learned hashing framework aims to learn a robust similarity metric so as to maximize the similarity of the same class discrete hash codes and minimize the similarity of the different class discrete hash codes simultaneously. The discriminant information of the training data can thus be incorporated into the learning framework. Meanwhile, the hash functions are constructed to fit the directly learned binary hash codes. Experimental results clearly demonstrate that the proposed method achieves leading performance compared with the state-of-the-art semi-supervised classification methods. (C) 2018 Elsevier Ltd. All rights reserved.
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