Texture analysis and classification with tree-structured wavelet transform

Abstract

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.

Invariant texture segmentation via circular Gabor filters

“…Furthermore Chang and Kuo [19] have already pointed out that texture features are often prevalent in the intermediate frequency bands. From this point we propose another frequency selection scheme, which emphasizes the intermediate frequency band Our choice of Gabor filters does not consider the very high frequency band of image (0.4-0.5).…”

“…One important finding is that the spatial-frequency bandwidth of the response of the human visual cortical cells ranges from 0.5 to 2. [19]:…”

Content based Trademark Retrieval by Integrating Shape with Colour and Texture Information

“…Tree Structured wavelet transform [13] decomposes sub signals in the low frequency channels. This method is highly efficient because of the innate property of images because of which the essential information of images exists in lower sub bands.…”

Brief review of invariant texture analysis methods

“…Chang and kuo [90] use the tree-structured wavelet transform for texture classiÿcation. Porter [61,62] develops the wavelet transform for invariant texture analysis based on the Daubechies four-tap wavelet ÿlter coecients.…”