关键词: ROI, 熵, 互信息, K-means, 图像分类

Abstract: A multi-features optimized image classification method based on region of interest (ROI) extracting method using color entropy extreme value and color entropy mutual information is proposed. Firstly, the most relevant region is determined by the color entropy extreme value, then the continuous ROI region is determined by using entropy mutual information to grow sub-region quickly. The Dense-SIFT characteristic description is extracted based on the ROI region, and a visual dictionary is generated by K-means method. In order to use the spatial local information, the pyramid matching method is adopted. Finally, the characteristics are input into SVM for classification. In the Caltech101 and Caltech256 databases, 8 data sets are selected for experiment. The average classification accuracy is improved by 6.86% obtained by using ROI extraction algorithm and the convergence rate is improved by nearly half. After adding the color entropy and the color third moments, the classification accuracy is further increased by 2.36%, it is 9.22% higher than before improvement totally.

Key words: region of interest, entropy, mutual information, K-means, image classification