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Published in:
2009 | OriginalPaper | Chapter
Texture Classification of the Entire Brodatz Database through an Orientational-Invariant Neural Architecture
Authors : F. J. Díaz-Pernas, M. Antón-Rodríguez, J. F. Díez-Higuera, M. Martínez-Zarzuela, D. González-Ortega, D. Boto-Giralda
Published in: Bioinspired Applications in Artificial and Natural Computation
Publisher: Springer Berlin Heidelberg
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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.