Ellipse Invariant Algorithm for Texture Classification

Abstract

The classification of texture images, especially those with spatial rotation and region shift, is a challenge and important problem in image analysis and classification. This paper proposes a novel algorithm design, an ellipse invariant algorithm, to improve the capability of texture classification for spatial rotation and region shift. The principle of an ellipse invariant algorithm is to use a minimum ellipse to enclose specific representative pixels extracted by the subtracting clustering method. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in original texture. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet, neighboring grey level dependence matrix and the ellipse invariant algorithm. Support vector machines SVMs are introduced as the classifier. The proposed hybrid texture filter can classify both the stochastic textures and structural textures. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.