Sparse presentation based classification with position-weighted block dictionary

Jun He; Tian Zuo; Bo Sun; Xuewen Wu; Lejun Yu; Fengxiang Ge; Chao Chen

doi:10.1117/12.2039610

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25 February 2014 Sparse presentation based classification with position-weighted block dictionary

Jun He, Tian Zuo, Bo Sun, Xuewen Wu, Lejun Yu, Fengxiang Ge, Chao Chen

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Proceedings Volume 9019, Image Processing: Algorithms and Systems XII; 90190X (2014) https://doi.org/10.1117/12.2039610
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

This paper is aiming at applying sparse representation based classification (SRC) on general objects of a certain scale. Authors analyze the characteristics of general object recognition and propose a position-weighted block dictionary (PWBD) based on sparse presentation and design a framework of SRC with it (PWBD-SRC). Principle and implementation of PWBD-SRC have been introduced in the article, and experiments on car models have been given in the article. From experimental results, it can be seen that with position-weighted block dictionary (PWBD) not only the dictionary scale can be effectively reduced, but also roles of image blocks taking in representing a whole image can be embodied to a certain extent. In reorganization application, an image only containing partial objects can be identified with PWBD-SRC. Besides, rotation and perspective robustness can be achieved. Finally, a brief description on some remaining problems has been proposed in the article.

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Jun He, Tian Zuo, Bo Sun, Xuewen Wu, Lejun Yu, Fengxiang Ge, and Chao Chen "Sparse presentation based classification with position-weighted block dictionary", Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190X (25 February 2014); https://doi.org/10.1117/12.2039610

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