Texture Classification using a Linear Configuration Model based Descriptor
Yimo Guo, Guoying Zhao, Matti Pietikäinen · 2011
We investigate rotation invariant image description and develop a linear model based descriptor namely MiC, which is suited to modeling microscopic configuration of images. To explore multi-channel discriminative information of both the microscopic configuration and local structures, the feature extraction process is formulated as an unsupervised framework that consists of: 1) the configuration model to encode image microscopic configuration; and 2) local patterns to describe local structural information. In this way, images are represented by a novel feature: local configuration pattern (LCP). We evaluate the performance of the proposed method by classifying textures present in three challenging texture databases: Outex_TC_00012, KTH-TIPS2 and Columbia-Utrecht (CUReT). The encouraging results show that LCPs is highly discriminative. 1