DCT Based Texture Classification Using Soft Computing Approach

TL;DR

This paper introduces a texture classification method using DCT coefficients combined with soft computing techniques, specifically neurocomputing and neuro-fuzzy computing, demonstrating that neuro-fuzzy models outperform neural networks.

Contribution

The paper presents a novel approach integrating DCT with neuro-fuzzy computing for improved texture classification accuracy.

Findings

Neuro-fuzzy model outperforms neural network in texture classification.

DCT effectively transforms color images into gray levels for analysis.

Prolonged training impacts neural network performance.

Abstract

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.