A robust descriptor based on Weber's Law

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends on not only the change of a stimulus (such as sound, lighting, et al.) but also the original intensity of the stimulus. Specifically, WLD consists of two components: its differential excitation and orientation. A differential excitation is a function of the ratio between two terms: One is the relative intensity differences of its neighbors against a current pixel; the other is the intensity of the current pixel. An orientation is the gradient orientation of the current pixel. For a given image, we use the differential excitation and the orientation components to construct a concatenated WLD histogram feature. Experimental results on Brodatz textures show that WLD impressively outperforms the other classical descriptors (e.g., Gabor). Especially, experimental results on face detection ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Jie Chen, Shiguang Shan, Guoying Zhao, Xilin Chen, Wen Gao, Matti Pietikäinen
• Keywords: Computer Vision, Current Pixel, CVPR 2008, Local Descriptor, Weber Local Descriptor