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In image classification and retrieval applications, images are represented by a set of features. These features are extracted from both spatial and wavelet transformed input images. The wavelet transform decomposes images into multiple resolutions by separating smooth and sharp information in individual channels to give more details about the image. Each level of smooth and sharp decomposed channels is individually involved in texture feature extraction. It is difficult to achieve better results in image classification and retrieval applications with fewer discriminant details of the image. Even though each level of the smooth channel correlates with its successive decomposition levels, these feature extraction methods do not consider the relationship between the multiple levels of decomposed images. This motivates the proposed work to extract the correlation between the different levels of wavelet decomposed images. The proposed work (i) encodes the local difference obtained from multiple radii across the different levels of wavelet decomposed channels, (ii) assesses the proposed texture feature extraction method using classification and retrieval experiments with five different wavelet filters over twelve image databases and (iii) analyses the importance of similarity measures involved in the proposed feature-based image retrieval experiments.