Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2016

Abstract

With the advance of internet and multimedia technologies, large-scale multi-modal representation techniques such as cross-modal hashing, are increasingly demanded for multimedia retrieval. In cross-modal hashing, three essential problems should be seriously considered. The first is that effective cross-modal relationship should be learned from training data with scarce label information. The second is that appropriate weights should be assigned for different modalities to reflect their importance. The last is the scalability of training process which is usually ignored by previous methods. In this paper, we propose Multi-graph Cross-modal Hashing (MGCMH) by comprehensively considering these three points. MGCMH is unsupervised method which integrates multi-graph learning and hash function learning into a joint framework, to learn unified hash space for all modalities. In MGCMH, different modalities are assigned with proper weights for the generation of multi-graph and hash codes respectively. As a result, more precise cross-modal relationship can be preserved in the hash space. Then Nyström approximation approach is leveraged to efficiently construct the graphs. Finally an alternating learning algorithm is proposed to jointly optimize the modality weights, hash codes and functions. Experiments conducted on two real-world multi-modal datasets demonstrate the effectiveness of our method, in comparison with several representative cross-modal hashing methods.

Keywords

Cross-modal hashing, Multi-graph learning, Cross-media retrieval

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

Multimedia Tools and Applications

Volume

75

Issue

15

First Page

9185

Last Page

9204

ISSN

1380-7501

Identifier

10.1007/s11042-016-3432-0

Publisher

Springer

Embargo Period

10-7-2019

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s11042-016-3432-0

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