Learning unified binary codes for cross-modal retrieval via latent semantic hashing-Reference-Cited by-同舟云学术

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Learning unified binary codes for cross-modal retrieval via latent semantic hashing

Published:2016-11 Issue: Volume:213 Page:191-203
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Xu Xing,He Li,Shimada Atsushi,Taniguchi Rin-ichiro,Lu Huimin

Funder

Grant-in-Aid for Scientific Research

Grant in Aid for Japan Society for the Promotion of Science

Open Fund of the Key Laboratory of Marine Geology and Environment in Chinese Academy of Sciences

Research Fund of State Key Laboratory of Marine Geology in Tongji University

Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference42 articles.

1. Canonical correlation analysis;Hardoon;Neural Comput.,2004

2. Modeling semantic aspects for cross-media image indexing;Monay;IEEE Trans. Pattern Anal. Mach. Intell.,2007

3. S.J. Hwang, K. Grauman, Accounting for the relative importance of objects in image retrieval, in: Proceedings of the British Machine Vision Conference (BMVC), 2010, pp. 1–12.

4. J. Song, Y. Yang, Y. Yang, Z. Huang, H.T. Shen, Inter-media hashing for large-scale retrieval from heterogeneous data sources, in: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD ’13, 2013, pp. 785–796.

5. N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. Lanckriet, R. Levy, N. Vasconcelos, A new approach to cross-modal multimedia retrieval, in: ACM International Conference on Multimedia, 2010, pp. 251–260.

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