Joint Image-Text Hashing for Fast Large-Scale Cross-Media Retrieval Using Self-Supervised Deep Learning

Wu, Gengshen and Han, Jungong and Lin, Zijia and Ding, Guiguang and Zhang, Baochang and Ni, Qiang (2019) Joint Image-Text Hashing for Fast Large-Scale Cross-Media Retrieval Using Self-Supervised Deep Learning. IEEE Transactions on Industrial Electronics, 66 (12). 9868 - 9877. ISSN 0278-0046

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Abstract

Recent years have witnessed the promising future of hashing in the industrial applications for fast similarity retrieval. In this paper, we propose a novel supervised hashing method for large-scale cross-media search, termed Self-Supervised Deep Multimodal Hashing (SSDMH), which learns unified hash codes as well as deep hash functions for different modalities in a self-supervised manner. With the proposed regularized binary latent model, unified binary codes can be solved directly without relaxation strategy while retaining the neighborhood structures by the graph regularization term. Moreover, we propose a new discrete optimization solution, termed as Binary Gradient Descent, which aims at improving the optimization efficiency towards real-time operation. Extensive experiments on three benchmark datasets demonstrate the superiority of SSDMH over state-of-the-art cross-media hashing approaches.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Industrial Electronics
Additional Information:
©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
127701
Deposited By:
Deposited On:
28 Sep 2018 09:00
Refereed?:
Yes
Published?:
Published
Last Modified:
08 Jul 2020 07:45