A multi-scale supervised orientational invariant neural architecture for natural texture classification

Authors:
F. J. Díaz-Pernas;M. Antón-Rodríguez;F. J. Perozo-Rondón;D. González-Ortega
Affiliations:
Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain;Department of Computational Sciences, University of Carabobo, Venezuela;Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Valladolid, Spain
Venue:
Neurocomputing
Year:
2011

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Abstract

A multi-scale supervised neural architecture, called Multi-Scale SOON, is proposed for natural texture classification. This architecture recognizes the input textured image through a hierarchical categorization structure in multiple scales. This process consists of three sequential phases: a multi-scale feature extraction, a scale prototype pattern generation, and a multi-scale prototype fusion pattern classification. First phase extracts scale textural features using the Gabor filtering. Then, a hierarchical categorization shapes the classification. First categorization level generates the scale prototypes and an upper level categorizes the prototypes fusion. Three increasing complexity tests over the well-known Brodatz database are performed in order to quantify the Multi-Scale SOON behavior. The comparison to other standout methods proves Multi-Scale SOON behavior to be satisfactory. The tests, including the entire texture album, show the stability and robustness of the Multi-Scale SOON response.