Fusing Subcategory Probabilities for Texture Classification

Yang Song, Weidong Cai, Qing Li, Fan Zhang, David Dagan Feng, Heng Huang; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4409-4417


Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research. Performance of texture classification 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 classification. By clustering each class into subcategories, classification probabilities at the subcategory-level 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 classification probability to obtain the final class label. Our method was applied to texture classification on three challenging datasets - KTH-TIPS2, FMD and DTD, and has shown excellent performance in comparison with the state-of-the-art approaches.

Related Material

author = {Song, Yang and Cai, Weidong and Li, Qing and Zhang, Fan and Dagan Feng, David and Huang, Heng},
title = {Fusing Subcategory Probabilities for Texture Classification},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}