期刊
IET IMAGE PROCESSING
卷 10, 期 1, 页码 61-67出版社
WILEY
DOI: 10.1049/iet-ipr.2015.0329
关键词
image classification; image coding; image matching; image representation; feature extraction; image texture; shape recognition; image classification; scale invariant feature transform sparse coding spatial pyramid matching representation; weighted centre-symmetric local ternary pattern feature extraction approach; image shape information; texture information; SIFT-WCS-LTP feature based ScSPM representation classification algorithm
类别
资金
- National Natural Science Foundation of China [61170116, 61375010, 61300075, 61472031]
- Beijing Higher Education Young Elite Teacher Project [YETP0375]
- Fundamental Research Funds for the Central Universities [FRF-TP-14-120A2]
- Educational Commission of Jiangxi province, China [GJJ14459]
Shape and texture information are critical to the accuracy of image classification systems. In this study, the authors propose a novel descriptor called weighted centre-symmetric local ternary pattern (WCS-LTP), better characterising the image local texture. Then, based on the proposed WCS-LTP descriptor, they introduce a new local scale invariant feature transform and WCS-LTP (SIFT-WCS-LTP) feature extraction approach. Compared with conventional local CS-LTP and SIFT features, the authors' proposed SIFT-WCS-LTP feature can not only capture the shape information of images, but also tend to extract more precise texture information. Finally, SIFT-WCS-LTP feature-based sparse coding spatial pyramid matching (ScSPM) representation classification is proposed for image classification. Extensive experimental results demonstrate that the effectiveness of their proposed SIFT-WCS-LTP feature-based ScSPM representation classification algorithm.
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