Cross-view hashing via supervised deep discrete matrix factorization. (July 2020)

• Record Type: Journal Article
• Authors: Xiong, Yingjun; Xu, Yan; Shu, Xin
• Is Part Of: Pattern recognition. Volume 103(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Matrix factorization -- Cross-view hashing -- Similarity search; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107270
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a discrete deep matrix factorization to learn unified hash codes which can capture more complex structure within heterogeneous data, resulting in more representative hashing codes. We further introduce a linear classification error term to make the learned unified hashing codes discriminative. We directly conduct discrete optimization which can reduce the quantization error. Abstract: Matrix factorization has been utilized for the task of cross-view hashing, where basis functions are learned to map data from different views to the same hamming embedding. It is possible that the basis functions between the hamming embedding and the original data matrix contain rather complex hierarchical information, which existing work can not capture. In addition, previous work employs relaxation technique in the matrix factorization based hashing which may lead to large quantization error. To address these issues, this paper presents a novel Supervised Discrete Deep Matrix Factorization (SDDMF) for cross-view hashing. We introduce deep matrix factorization so that SDDMF is able to learn a set of hierarchical basis functions and unified binary codes from different views. In addition, a classification error term is incorporated into the objective to learn discriminative binary codes. We then employ a linearization technique to directly optimize the discrete constraints which can significantly reduce the quantization error. Experimental results on three standard datasets with image-text modalities verify that SDDMF significantly outperforms several state-of-the-art methods.