Issue No. 09 - September (2010 vol. 32)
Jie Chen , University of Oulu, Finland
Shiguang Shan , Chinese Academy of Sciences, Beijing
Chu He , Wuhan University, Wuhan
Guoying Zhao , University of Oulu, Finland
Matti Pietikäinen , University of Oulu, Finland
Xilin Chen , Chinese Academy of Sciences, Beijing
Wen Gao , Peking University, Beijing
Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
Pattern recognition, Weber law, local descriptor, texture, face detection.
J. Chen et al., "WLD: A Robust Local Image Descriptor," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1705-1720, 2009.