Classification of color textures with random field models and neural networks

• Autores: Orlando J. Hernandez, John Cook, Michael Griffin, Cynthia De Rama, Michael McGovern
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 5, Nº. 3, 2005 (Ejemplar dedicado a: Fifteenth Issue), págs. 150-157
• Idioma: inglés
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• Resumen

  • A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.