Texture classification based on DCT and soft computing
Sorwar, G, Abraham, A & Dooley, LS 2001, 'Texture classification based on DCT and soft computing', Proceedings of 10th IEEE International Conference on Fuzzy Systems, Melbourne, Vic., 2-5 December, IEEE, vol. 2, pp. 545-548.
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 the DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images with DCT, we used two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing. We used a feedforward neural network trained by backpropagation algorithm 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.