IEEE TII -yang -2017.pdf (14.99 MB)
A fast image retrieval method designed for network big data
journal contribution
posted on 2017-02-23, 15:11 authored by Jiachen Yang, Bin Jiang, Baihua LiBaihua Li, Kun Tian, Zhihan LvIn 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.
History
School
- Science
Department
- Computer Science
Published in
IEEE Transactions on Industrial InformaticsPages
1 - 1Citation
YANG, J. ...et al., 2017. A fast image retrieval method designed for network big data. IEEE Transactions on Industrial Informatics, 13 (15), pp.2350-2359Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2017-01-24Notes
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 worksISSN
1551-3203eISSN
1941-0050Publisher version
Language
- en