Color texture classification based on gravitational collapse

Authors:
Jarbas Joaci De Mesquita Sá Junior;André Ricardo Backes;Paulo CéSar Cortez
Affiliations:
Departamento de Engenharia de Teleinformática-DETI, Centro de Tecnologia-UFC, Campus do Pici, S/N, Bloco 725, Caixa Postal 6007, CEP: 60.455-970, Fortaleza, Brazil;Faculdade de Computação, Universidade Federal de Uberlíndia, Av. João Naves de Ávila, 2121, 38408-100, Uberlíndia, MG, Brazil;Departamento de Engenharia de Teleinformática-DETI, Centro de Tecnologia-UFC, Campus do Pici, S/N, Bloco 725, Caixa Postal 6007, CEP: 60.455-970, Fortaleza, Brazil
Venue:
Pattern Recognition
Year:
2013

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Abstract

Texture and color are essential attributes to be analyzed for any robust computer vision system. This paper presents a novel method to analyze color-texture images, based on representing states of a simplified gravitational collapse from each image color channel and extracting information from each state using the Bouligand-Minkowski fractal dimension and the lacunarity method. In this approach, we obtained the best classification results when the images of each channel evolved in times t={1,5,10,15}, each time representing a state, using radius r={3,4,5,6} for the Bouligand-Minkowski method and box size l={2,3,4,5,6} for the lacunarity method. The best classification results were 99.37% and 96.57% of success rate (percentage of samples correctly classified) for VisTex and USPTex databases, respectively. These results prove that the proposed approach opens a promising source of research in color texture analysis still to be explored.