Abstract

Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research. Performance of texture classifification is however still lacking for some challenging cases, largely due to the high intra-class variation and low inter-class distinction. To tackle these issues, in this paper, we propose a sub-categorization model for texture classifification. By clustering each class into subcategories, classifification probabilities at the subcategorylevel are computed based on between-subcategory distinctiveness and within-subcategory representativeness. These subcategory probabilities are then fused based on their contribution levels and cluster qualities. This fused probability is added to the multiclass classifification probability to obtain the fifinal class label. Our method was applied to texture classifification on three challenging datasets – KTH-TIPS2, FMD and DTD, and has shown excellent performance in comparison with the state-of-the-art approaches.