Unsupervised Texture Segmentation: Comparison of Texture Features

Texture is an important image-content that has been utilized for different machine intelligent tasks, like those in machine vision and remote sensing, which identify objects of interest by segmenting the image texture. This paper aims at comparing texture features based on Discrete Fourier Transform (DFT) with ones based on Gabor wavelets for unsupervised image segmentation. The comparison is realized theoretically, analytically, as well as empirically. Images of natural scenes from a standard image database have been taken as test images. Analytical comparison shows that the DFT-based features are computationally less expensive than those based on Gabor wavelets. Empirical results show that the performance of the texture features based on DFT is comparable to those based on Gabor wavelets.