WLD: A Robust Local Image Descriptor

Authors:
Jie Chen;Shiguang Shan;Chu He;Guoying Zhao;Matti Pietikainen;Xilin Chen;Wen Gao
Affiliations:
University of Oulu, Finland;Chinese Academy of Sciences, Beijing;Wuhan University, Wuhan;University of Oulu, Finland;University of Oulu, Finland;Chinese Academy of Sciences, Beijing;Peking University, Beijing
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2010

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Abstract

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.