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Proceedings Paper

New image distance and its application in object recognition
Author(s): Bing Yang; Jun Zhang; Dajiang Shen; Jinwen Tian; Yongcai Liu
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Paper Abstract

This paper presents a new distance measure for image matching based on local Kullback-Leibler divergence, which we call Image Kullback-Leibler Distance (IKLD). Unlike traditional methods, IKLD takes account into not only the spatial relationships of pixels, but also the structure information around pixels. Therefore, it is robust enough to small changes in viewpoint. In order to illustrate its performance, we imbed it into support vector machines for view-based object recognition. Experimental results based on the COIL-100 show that it outperforms most existing techniques, such as traditional PCA+LDA (principal component analysis, linear discriminant analysis), non-linear SVM, Discriminant Tensor Rank-One Decomposition (DTROD) and Sparse Network of Winnows (SNoW).

Paper Details

Date Published: 15 November 2007
PDF: 6 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678815 (15 November 2007); doi: 10.1117/12.749041
Show Author Affiliations
Bing Yang, Huazhong Univ. of Science and Technology (China)
Jun Zhang, Huazhong Univ. of Science and Technology (China)
Dajiang Shen, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)
Yongcai Liu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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