Texture analysis and classification with tree-structured wavelet transform

One difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms help to overcome this difficulty. In this research, we propose a multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets for texture analysis and classification. The development of this new 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, we are able to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition only in low frequency channels. We develop a progressive texture classification algorithm which is not only computationally attractive but also has excellent performance. The performance of our new method is compared with that of several other methods using the DCT, DST, DHT, pyramid-structured wavelet transforms, Gabor filters, and Laws filters.