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).