Cascading Convolutional Color Constancy

Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it’s still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection properties of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4 ) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.

Authors

• Huanglin Yu
• Ke Chen The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
• Kaiqi Wang
• Yanlin Qian
• Zhaoxiang Zhang Center for Artificial Intelligence and Robotics, HKISI-CAS Institute of Automation, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Kui Jia School of Data Science, The Chinese University of Hong Kong, Shenzhen

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