DCT based texture classification using soft computing approach
Classification of texture patterns 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 images. As DCT works on gray level images, 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 algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the 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 the neural networks. It is observed that the proposed neuro-fuzzy model performed better than the neural network.
Sorwar, G & Abraham, A 2004, 'DCT based texture classification using soft computing approach', Malaysian Journal of Computer Science, vol. 17, no. 1, pp. 13-23.
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