Gabor Kernels for Textured Image Representation and Classification
A Gabor based representation for textured images is proposed. Instead of the ordinary filter bank, a reproducing kernel representation is constructed consisting of a sum of several local reproducing kernels. The image representation coefficients are computed by a basis pursuit procedure, and are then considered as the feature vectors. The feature vectors are used to construct a kernel for a support vector classifier. Results are presented for a set of oriented texture images.
KeywordsFeature Vector Texture Image Image Representation Reproduce Kernel Hilbert Space Kernel Representation
- 5.Varma, M., Zisserman, A.: A statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision 62, 61–81 (2005)Google Scholar
- 8.Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
- 9.Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
- 12.Czyzyk, J., Mehrotra, S., Wagner, M., Wright, S.: PCx User Guide, Optimization Technology Center, Technical Report OTC 96/01 (1997)Google Scholar
- 13.Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)Google Scholar