Optimizing Gabor Filter Design for Texture Edge Detection and Classification
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An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.
KeywordsTexture Gabor kernels Edge detection Classification
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- Fogel, I., & Sagi, D. (1989). Gabor filters as texture discriminator. BioCyber, 61, 102–113. Google Scholar
- Greenspan, H., Belongie, S., Perona, P., Goodman, R., Rackshit, S., & Anderson, C. H. (1994). Overcomplete steerable pyramid filters and rotation invariance. In CVPR (pp. 222–228). Google Scholar
- Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV (Vol. II, pp. 416–423). Google Scholar
- Sandler, R., & Lindenbaum, M. (2006). Gabor filter analysis for texture segmentation. In CVPRW (p. 178), June 2006. Google Scholar
- Varma, M., & Zisserman, A. (2002). Classifying images of materials: achieving viewpoint and illumination independence. European Conference on Computer Vision, 3, 255–271. Google Scholar
- Varma, M., & Zisserman, A. (2003). Texture classification: are filter banks necessary? In CVPR (Vol. 2, pp. 691–698), June 2003. Google Scholar