标题:Content-Based Image Retrieval Based on Multi-feature Fusion Optimized by Brain Storm Optimization
作者:Zhou, Hengjun ;Jiang, Mingyan
作者机构:[Zhou, Hengjun ;Jiang, Mingyan ] School of Information Science and Engineering, Shandong University, Jinan; 250100, China
会议名称:2017 International Conference on Computing Intelligence and Information System, CIIS 2017
会议日期:21 April 2017 through 23 April 2017
来源:Proceedings - 2017 International Conference on Computing Intelligence and Information System, CIIS 2017
出版年:2018
卷:2018-January
页码:72-78
DOI:10.1109/CIIS.2017.20
关键词:Brain storm optimization; Content based image retrieval; Feature extraction; Multi-feature fusion
摘要:With the fast development of information technology and increasing number of image database, how to retrieval a large amount of information from the image quickly and effectively becomes more and more important. Brain Storm Optimization (BSO) is simple, robust and has high searching precision. So it is applied into the image retrieval in this paper. Content-based image retrieval (CBIR) extracts the color, texture, shape and other low-level visual features of images to realize image matching. Compared with image retrieval based on single feature, image retrieval based on multi-feature fusion can fully represent image information. In the multi-feature fusion, the ratio of each feature selection is critical to the search result. Traditional method is manually set or proportional integration, which ignores the priority between the various features. This paper uses BSO for image retrieval. Color histogram, color moment, color structure descriptor, Tamura texture feature, GLCM texture, wavelet transform texture, Gabor transform texture, edge histogram descriptors and Hu invariant moment are extracted in the paper and BSO is used for image retrieval. Experiment results show that the proposed method can retrieval the target image accurately and improve the precision of the system. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050524280&doi=10.1109%2fCIIS.2017.20&partnerID=40&md5=066d3cea1caf5dbcf46874e93bd22d8e
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