LINEAR SPATIAL PYRAMID MATCHING USING NON-CONVEX AND NON-NEGATIVE SPARSE CODING FOR IMAGE CLASSIFICATION

被引：0
|
作者：
Bao, Chengqiang [1 ]
He, Liangtian [1 ]
Wang, Yilun [1 ,2 ,3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Informat BioMed, Chengdu 610054, Peoples R China
[3] Cornell Univ, Ctr Appl Math, Ithaca, NY 14850 USA
来源：
2015 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING | 2015年
关键词：
Image classification; Non-convex and non-negative sparse coding; SPM; Iterative support detection;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Recently sparse coding have been highly successful in image classification mainly due to its capability of incorporating the sparsity of image representation. In this paper, we propose an improved sparse coding model based on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform (SIFT) descriptors. The novelty is the simultaneous non-convex and non-negative characters added to the sparse coding model. Our numerical experiments show that the improved approach using non-convex and non-negative sparse coding is superior than the original ScSPM[1] on several typical databases.
引用
收藏
页码：186 / 190
页数：5
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