A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition in low-frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed. The performance of the present method is compared with that of several other methods.

Traditional texture analysis algorithms focus too much on the local coupling between image pixels. Time/frequency analytical tools such as the Gabor and wavelet transforms can efficiently characterize the coupling of different scales in textures, and help to overcome this difficulty. However, the conventional wavelet transform, which has a finer resolution in the lower frequency channels, does not work properly for textured images, since textures are quasi-periodic signals whose dominant frequencies are located in the middle frequency regions. In the present research, a tree-structured wavelet transform is proposed. An adaptive procedure is developed to zoom into any frequency channels with significant information so that the decomposition can be further performed. The application of the new transform to texture classification is demonstrated.>

Proposes a multiresolution approach based on a tree-structured wavelet transform for texture classification. The development of tree-structured wavelet transform is motivated by the observation that textures are quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, one is able to zoom into desired frequency channels and performs further decomposition. In contrast, the conventional wavelet transform only decomposes subsignals in low frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed.>