关键词: 纹理分类, 可变局部边缘模式, 主导学习框架, 全局主导模式集

Abstract: Edge is an effective feature for similar texture classification. For better improving the edge detection accuracy, Varied Local Edge Pattern（VLEP） algorithm is used to extract the texture edge feature in which multi-scale and multi-direction properties are taken into account. Generally, texture feature extraction is based on regional statistics of the intensity change between the pixel and its neighbors. However, large deviation often exists, especially when the image resolution changes, or when the image is lighted and reflected. Therefore, in this paper dominant learning framework approach based on the VLEP is proposed to classify similar texture images, which constructs global dominant set to solve the problem of small inter-class and large intra-class distance resulting from the above deviation. The experimental results show that the proposed method can effectively improve the classification performance of similar texture image.

Key words: texture classification, Varied Local Edge Pattern（VLEP）, dominant learning framework, global dominant pattern set