A Classification Methodology for Color Textures Using Multispectral Random Field Mathematical Models

A large number of texture classification approaches have been developed in the past but most of these studies target gray-level textures. In this paper, supervised classification of color textures is considered. Several different Multispectral Random Field models are used to characterize the texture. 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 sixteen different color textures. A near 100% classification accuracy is achieved. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using gray-level random field models. It is shown that color based classification is significantly more accurate than its gray-level counterpart.