Deep Self-Taught Hashing for Image Retrieval

Liu, Yu, Song, Jingkuan, Zhou, Ke, Yan, Lingyu, Liu, Li, Zou, Fuhao and Shao, Ling (2019) Deep Self-Taught Hashing for Image Retrieval. IEEE Transactions on Cybernetics, 49 (6). pp. 2229-2241. ISSN 2168-2267

[img]
Preview
PDF (Accepted manuscript) - Submitted Version
Download (2MB) | Preview

Abstract

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 21 May 2018 11:30
Last Modified: 18 Jul 2020 23:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/67140
DOI: 10.1109/TCYB.2018.2822781

Actions (login required)

View Item View Item