25 February 2014 Sparse presentation based classification with position-weighted block dictionary
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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, Chao Chen, "Sparse presentation based classification with position-weighted block dictionary", Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190X (25 February 2014); doi: 10.1117/12.2039610; https://doi.org/10.1117/12.2039610
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