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Proceedings Paper

Image categorization based on visual saliency and Bag-of-Words model
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Paper Abstract

Large-scale image categorization is a challenging task. In this paper, we propose a new image categorization approach based on visual saliency and bag-of-words model. Firstly, a saliency map is generated by visual saliency method that exploits some coarsely localized information, i.e. the salient region shape and contour. Secondly, size of salient region is acquired by calculating maximum entropy. Thirdly, the local image descriptor-SIFT extracted in the salient region and visual saliency information are combined to build visual words. Finally, the visual word bag is categorized by Support Vector Machine. By comparing with BOW model categorization methods, experiment results show that our methods can effectively improve the accuracy of image categorization.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300M (14 February 2020); doi: 10.1117/12.2538139
Show Author Affiliations
Wenxiang Li, Wuhan Institute of Technology (China)
Yanfei Chen, Wuhan Institute of Technology (China)
Zechang Wu, Wuhan Institute of Technology (China)
Hongsheng Peng, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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