Discriminative coupled dictionary hashing for fast cross-media retrieval

Yu, Z; Wu, F; Yang, Y; Tian, Q; Luo, J; Zhuang, Y

Discriminative coupled dictionary hashing for fast cross-media retrieval

Yu, Z Wu, F Yang, Y

Tian, Q Luo, J Zhuang, Y

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Publication Type:: Conference Proceeding
Citation:: SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp. 395 - 404
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yu, Z	en_US
dc.contributor.author	Wu, F	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Tian, Q	en_US
dc.contributor.author	Luo, J	en_US
dc.contributor.author	Zhuang, Y	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp. 395 - 404	en_US
dc.identifier.isbn	9781450322591	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120905
dc.description.abstract	Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively "weak" modalities into DCDH and extend it to multiview DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV- DCDH outperform the state-of-the-art methods significantly on cross-media retrieval. Copyright 2014 ACM.	en_US
dc.relation.ispartof	SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval	en_US
dc.relation.isbasedon	10.1145/2600428.2609563	en_US
dc.title	Discriminative coupled dictionary hashing for fast cross-media retrieval	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively "weak" modalities into DCDH and extend it to multiview DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV- DCDH outperform the state-of-the-art methods significantly on cross-media retrieval. Copyright 2014 ACM.

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http://hdl.handle.net/10453/120905