Discriminant cross-modal hashing

Xu, Xing, Shen, Fumin, Yang, Yang and Shen, Heng Tao (2016). Discriminant cross-modal hashing. In: ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval. 6th ACM International Conference on Multimedia Retrieval, ICMR 2016, New York, NY, United States, (305-308). 6 - 9 June 2016. doi:10.1145/2911996.2912056

Author Xu, Xing
Shen, Fumin
Yang, Yang
Shen, Heng Tao
Title of paper Discriminant cross-modal hashing
Conference name 6th ACM International Conference on Multimedia Retrieval, ICMR 2016
Conference location New York, NY, United States
Conference dates 6 - 9 June 2016
Convener ACM
Proceedings title ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval
Journal name Icmr'16: Proceedings of the 2016 Acm International Conference On Multimedia Retrieval
Place of Publication New York, NY, United States
Publisher Association for Computing Machinery
Publication Year 2016
Sub-type Fully published paper
DOI 10.1145/2911996.2912056
Open Access Status Not yet assessed
ISBN 9781450343596
Start page 305
End page 308
Total pages 4
Language eng
Abstract/Summary Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels. Existing hashing based methods generally project heterogeneous features to a common space for hash codes generation, and the supervising information is incrementally used for improving performance. However, these methods may produce ineffective hash codes, due to the failure to explore the discriminative property of supervising information and to effectively bridge the semantic gap between different modalities. To address these challenges, we propose a novel hashing based method in a linear classification framework, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific hash function, the unified binary codes and a linear classifier. Extensive experiments on three benchmark datasets highlight the advantage of the proposed method and show that it achieves the state-of-the-art performance.
Keyword Cross-modal hashing
Cross-modal retrieval
Discriminant analysis
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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