Weber's Law Descriptor

In collaboration with the Institute of Computing Technology of the Chinese Academy of Sciences, we proposed 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 texture analysis and face detection problems provided excellent performance. Recently, we combined LBP and WLD for the segmentation of dynamic textures and provided very good segmentation results compared to the state-of-the-art.