Title :
Color Constancy Using Double-Opponency
Author :
Shao-Bing Gao ; Kai-Fu Yang ; Chao-Yi Li ; Yong-Jie Li
Author_Institution :
Sch. of Life Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intraand inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.
Keywords :
image colour analysis; lighting; DO cells; DOCC model; HVS; LMS space; SO cells; color constancy model; color-biased images; double-opponency based color constancy; human visual system; illuminant color; light source color; retina; single-opponent cells; Biological system modeling; Image color analysis; Least squares approximations; Neurons; Radio frequency; Retina; Visualization; Color constancy; color constancy; double opponency; human visual system; pooling mechanism;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2015.2396053