New image distance and its application in object recognition

Bing Yang; Jun Zhang; Dajiang Shen; Jinwen Tian; Yongcai Liu

doi:10.1117/12.749041

15 November 2007 New image distance and its application in object recognition

Bing Yang, Jun Zhang, Dajiang Shen, Jinwen Tian, Yongcai Liu

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678815 (2007) https://doi.org/10.1117/12.749041
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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).

Citation Download Citation

Bing Yang, Jun Zhang, Dajiang Shen, Jinwen Tian, and Yongcai Liu "New image distance and its application in object recognition", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678815 (15 November 2007); https://doi.org/10.1117/12.749041

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