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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24514

Title: Internet cross-media retrieval based on deep learning
Authors: Jiang, Bin
Yang, Jiachen
Lv, Zhihan
Tian, Kun
Meng, Qinggang
Yan, Yan
Keywords: Cross-media retrieval
Deep learning
Feature extracting
Multimedia information
Issue Date: 2017
Publisher: © Elsevier
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.
Abstract: 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.
Description: This paper is in closed access until 20th Feb 2018.
Sponsor: 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).
Version: Accepted for publication
DOI: 10.1016/j.jvcir.2017.02.011
URI: https://dspace.lboro.ac.uk/2134/24514
Publisher Link: http://dx.doi.org/10.1016/j.jvcir.2017.02.011
ISSN: 1095-9076
Appears in Collections:Closed Access (Computer Science)

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