Unsupervised Texture Classification by Combining Multi-Scale Features and K-Means Classifier
Yong Hu, Chunxia Zhao · 2009
In the field of unsupervised texture classification, a combination of various families of methods was usually used for better classification results. However, the existing methods are usually used for specific application and evaluated with fixed window size. In this literature, we propose an effort to combine multi-scale features for unsupervised texture classification. The local binary pattern (LBP) is used for detecting micro textured structures. As for large scale texture information, Haralick features extracted from gray level co-occurrence matrix (GLCM) are adopted. In order to determine the optimal window size, each method is evaluated with different window sizes. By combining the information provided by multi-scale features for classification, the proposed method achieved higher classification rate than each single method evaluated over fixed window size. Experimental results confirmed the usefulness of this combination.