JVCIR-last submission.pdf (11.14 MB)
Internet cross-media retrieval based on deep learning
journal contribution
posted on 2017-03-24, 09:17 authored by Bin Jiang, Jiachen Yang, Zhihan Lv, Kun Tian, Qinggang MengQinggang Meng, Yan YanWith 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 RepresentationCitation
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
© ElsevierVersion
- 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
2017-02-12Publication date
2017Notes
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.2017.02.011ISSN
1095-9076Publisher version
Language
- en