Scaling and rotating texture classification based on JSEG algorithm
Chih-Chia Yao, Ya-Yun Yu · 2012
In this paper a novel algorithm is proposed for classifying variant textures resulting from scale magnification, narrowing caused by cutting into the original size, or spatial rotation. These variations usually occur in images captured by a camera using different focal distances. This study proposes a representative region-matched algorithm for improving texture classification after magnification, narrowing, and spatial rotation. The principle of a representative region-matched algorithm is to use a minimum ellipse to enclose a specific region extracted by the JSEG algorithm. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in the original texture. Besides, by adopting scale invariant parameters, the scale variant representative region can be classified. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet and the representative region matched algorithm. Support vector machines (SVMs) are introduced as the classifier. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.