Wavelet Texture Analysis Based On Best Feature Selection for Texture Classification

The development of a variety of image sensors, multivariate statistical techniques and image processing technologies has brought about the emergence of automated systems for evaluating visual quality. Feature extraction from images using wavelet transforms is an essential step in the methodology for an automated image grading system. In previous works, wavelet texture analysis (WTA) based on the discrete wavelet transform (DWT) has been recognized as one of the most successful feature extraction methods for classifying steel quality. In this work, we propose the best-basis approach. Our experiments provide two findings: First, the method based on the wavelet packet transform is more useful in characterizing steel quality than the previous DWT-based method. The wavelet packet transform are more powerful than other methods due to its equal frequency bandwidth. Second, the best-basis approach, which requires only a small number of features, is superior to the full packet methodology.