Wavelet Based Texture Analysis And Classification With Linear Regration Model

The Wavelet Transform is a multiresolution analysis tool commonly applied to texture analysis and classification and also Wavelet based pre-processing is a very successful method providing proper Image Enhancement and remove noise without considerable change in overall intensity level. The Wavelet Transform mostly used for contrast enhancement in noisy environments. In this paper we propose a texture analysis with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the samples images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Our method considers not only the frequency regions but also the correlation between these regions. In contrast the tree structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the, TSWT, Gabor Transform and GLCM with Gabor and some recently proposed methods