- Title
- MESH : a flexible manifold-embedded semantic hashing for cross-modal retrieval
- Creator
- Zhong, Fangming; Wang, Guangze; Chen, Zhikui; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184129
- Identifier
- vital:16460
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.3015528
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 147569-147579
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ 2019
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Cross-modal hashing; Discrete optimization; Manifold embedding; Semantic
- Full Text
- Reviewed
- Funder
- This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0831305
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