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Title: A fast image retrieval method designed for network big data
Authors: Yang, Jiachen
Jiang, Bin
Li, Baihua
Tian, Kun
Lv, Zhihan
Keywords: Big data
Image retrieval
Feature ranking
Distance learning
Issue Date: 2017
Publisher: © IEEE
Citation: YANG, J. ...et al., 2017. A fast image retrieval method designed for network big data. IEEE Transactions on Industrial Informatics, 13 (15), pp.2350-2359
Abstract: In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results.
Description: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Version: Accepted for publication
DOI: 10.1109/TII.2017.2657545
URI: https://dspace.lboro.ac.uk/2134/24204
Publisher Link: http://dx.doi.org/10.1109/TII.2017.2657545
ISSN: 1551-3203
Appears in Collections:Published Articles (Computer Science)

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