  Classification of texture pattern is one of the most important problems in
pattern recognition. In this paper, we present a classification method based on
the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works
on gray level image, the color scheme of each image is transformed into gray
levels. For classifying the images using DCT we used two popular soft computing
techniques namely neurocomputing and neuro-fuzzy computing. We used a
feedforward neural network trained using the backpropagation learning and an
evolving fuzzy neural network to classify the textures. The soft computing
models were trained using 80% of the texture data and remaining was used for
testing and validation purposes. A performance comparison was made among the
soft computing models for the texture classification problem. We also analyzed
the effects of prolonged training of neural networks. It is observed that the
proposed neuro-fuzzy model performed better than neural network.
