Texture Classification of the Entire Brodatz Database through an Orientational-Invariant Neural Architecture
This paper presents a supervised neural architecture, called SOON, for texture classification. Multi-scale Gabor filtering is used to extract the textural features which shape the input to a neural classifier with orientation invariance properties in order to accomplish the classification. Three increasing complexity tests over the well-known Brodatz database are performed to quantify its behavior. The test simulations, including the entire texture album classification, show the stability and robustness of the SOON response.
KeywordsLittle Square Support Vector Machine Training Rate Neural Architecture Texture Scene Texture Segregation
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