Pairwise relationship guided deep hashing for cross-modal retrieval
- Publication Type:
- Conference Proceeding
- Citation:
- 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 1618 - 1625
- Issue Date:
- 2017-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 14326-66443-1-PB.pdf | Published version | 1.18 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. With benefits of low storage cost and fast query speed, crossmodal hashing has received considerable attention recently. However, almost all existing methods on cross-modal hashing cannot obtain powerful hash codes due to directly utilizing hand-crafted features or ignoring heterogeneous correlations across different modalities, which will greatly degrade the retrieval performance. In this paper, we propose a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities. Our architecture integrates different types of pairwise constraints to encourage the similarities of the hash codes from an intra-modal view and an inter-modal view, respectively. Moreover, additional decorrelation constraints are introduced to this architecture, thus enhancing the discriminative ability of each hash bit. Extensive experiments show that our proposed method yields state-of-the-art results on two cross-modal retrieval datasets.
Please use this identifier to cite or link to this item: