A Method of Image Affective Semantic Classification Based on Attribution Reduction

Abstract

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.