title: WLD: a robust local image descriptor.
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.
authors.affiliation: Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, PO Box 4500, FI-90014, Finland. jiechen@ee.oulu.fi
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.forename: Shiguang
authors.initials: S
authors.lastname: Shan
authors.forename: Chu
authors.initials: C
authors.lastname: He
authors.forename: Guoying
authors.initials: G
authors.lastname: Zhao
authors.forename: Matti
authors.initials: M
authors.lastname: Pietikäinen
authors.forename: Xilin
authors.initials: X
authors.lastname: Chen
authors.forename: Wen
authors.initials: W
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique