The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - September (2010 vol.32)
pp: 1705-1720
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
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.
INDEX TERMS
Pattern recognition, Weber law, local descriptor, texture, face detection.
CITATION
Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen, Xilin Chen, Wen Gao, "WLD: A Robust Local Image Descriptor", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 9, pp. 1705-1720, September 2010, doi:10.1109/TPAMI.2009.155
REFERENCES
[1] M. Agrawal, K. Konolige, and M.R. Blas, "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching," Proc. European Conf. Computer Vision, 2008.
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool