Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77711
Title: Supervised discrete discriminant hashing for image retrieval
Authors: Cui, Y 
Jiang, J 
Lai, Z 
Hu, Z
Wong, W 
Keywords: Discrete hash codes
Discrete hash learning
Discriminant information
Robust similarity metric
Supervised hash learning
Issue Date: 2018
Publisher: Elsevier
Source: Pattern recognition, 2018, v. 78, p. 79-90 How to cite?
Journal: Pattern recognition 
Abstract: 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.
URI: http://hdl.handle.net/10397/77711
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2018.01.007
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