Supervised discrete discriminant hashing for image retrieval. (June 2018)

• Record Type: Journal Article
• Authors: Cui, Yan; Jiang, Jielin; Lai, Zhihui; Hu, Zuojin; Wong, WaiKeung
• Is Part Of: Pattern recognition. Volume 78(2018:Jun.)
• Page Start: 79
• Page End: 90
• Publication Date: 2018-06
• Subjects: Supervised hash learning -- Discrete hash learning -- Discrete hash codes -- Discriminant information -- Robust similarity metric; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.01.007
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We develop a new 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. To make the learned hash function for achieving optimal approximate discrete hash codes, the hash functions are optimized based on the directly learned discrete hash codes. 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.