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Optimized filters for efficient multi-texture discrimination
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DOI：10.1007/s10044-013-0339-5.png)
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When performing texture analysis via standard filter banks, good discrimination depends on the usage of a large number of filters. For example, when using the popular Gabor Filter Banks, the typical number of filters ranges from about ten to fifty. For applications requiring high frame rate processing, this is too complex. Also, discrimination may be poor if the method is not adapted to the characteristics of the target textures. Optimized filters attempt to solve these issues by automatically creating filters that are tuned to the target textures. Although these filters have shown to perform well when the number of textures to discriminate is small, their computational complexity increases dramatically in situations that arise in practice, e.g., those exhibiting ten or more classes of textures. In this paper, we propose optimized filters for efficient multi-texture discrimination. In particular, we propose two alternative filter designs: one-dimensional filters, applied horizontally and vertically, for orientation-dependent discrimination; and ring-shaped filters for rotationally invariant discrimination. Texture classification is based on the first four moments of the filter outputs, which are simple to compute yet approximate more sophisticated methods. The filter parameters are tuned through supervised learning, which is performed by using a Genetic Algorithm, that deals well with the non-convex nature of the objective function. We test our method with the Brodatz and VisTex albums, concluding that it outperforms state-of-the-art methods in terms of computational simplicity and accuracy.
Keyword:
Texture discrimination
Spatial filter
Julesz theory
Learning
Genetic algorithm
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