A New Method for Coarse Classification of Textures and Class Weight Estimation for Texture Retrieval

In this paper, a new texture classification method is provided for dividing texture images into three classes: periodic, directional, and random. The method is based on the fact that for a directional texture image, the magnitudes of its Fourier spectrum will concentrate on a certain direction; for periodic, on several directions; and for random, spread out over all directions. To use this fact, Fourier transform is, first, performed. Principal component analysis is, then, conducted on the Fourier spectrum to get the ratio of two eigenvalues, which will be used to measure the directionality of the texture image. If the texture image is not a directional one, for getting better discriminative properties for separating periodic textures from random ones, Fourier transform is applied to the Fourier spectrum to produce an enhanced Fourier spectrum. A discriminative measure based on the variance of the radial wedge distribution is, then, calculated and applied to classify the texture image as periodic or random one. Texture images from Brodatz album and Corel image database are used to demonstrate the effectiveness of the proposed method. In addition, it is shown that the intermediate results of the proposed method can be used to derive a weighting scheme used for texture retrieval. The proposed method can also be used to implement the texture browsing descriptor of MPEG-7. Received June 7, 2002