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Internet cross-media retrieval based on deep learning

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journal contribution
posted on 24.03.2017, 09:17 by Bin Jiang, Jiachen Yang, Zhihan Lv, Kun Tian, Qinggang Meng, Yan Yan
With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval.

Funding

This research is partially supported by National Natural Science Foundation of China (No. 61471260 and No. 61271324), and Natural Science Foundation of Tianjin (No. 16JCYBJC16000).

History

School

  • Science

Department

  • Computer Science

Published in

Journal of Visual Communication and Image Representation

Citation

JIANG, B. ...et al., 2017. Internet cross-media retrieval based on deep learning. Journal of Visual Communication and Image Representation, 48, pp.356-366.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

12/02/2017

Publication date

2017

Notes

This paper was accepted for publication in the journal Journal of Visual Communication and Image Representation and the definitive published version is available at http://dx.doi.org/10.1016/j.jvcir.2017.02.011

ISSN

1095-9076

Language

en

Exports