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
Affiliations:
Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain
Venue:
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Year:
2009

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Abstract

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.