Material Classification Using Random Forest
Zi Ming Zhao, Cui Hua Li, Hua Wang Shi, Quan Zou · Advanced materials research · 2011
Random forest has demonstrated excellent performance to deal with many problems of computer vision, such as image classification and keypoint recognition. This paper proposes an approach to classify materials, which combines random forest with MR8 filter bank. Firstly, we employ MR8 filter bank to filter the texture image. These filter responses are taken as texture feature. Secondly, Random forest grows on sub-window patches which are randomly extracted from these filter responses, then we use this trained forest to classify a given image (under unknown viewpoint and illumination) into texture classes. We carry out experiments on Columbia-Utrecht database. The experimental results show that our method successfully solves plain texture classification problem with high computational efficiency.