Mixed Pooling Neural Networks for Color Constancy

Damien Fourure (1, 2) , Rémi Emonet (2) , Elisa Fromont (2) , Damien Muselet (2) , Alain Trémeau (2) , Christian Wolf (1)

1 imagine - Extraction de Caractéristiques et Identification
2 LHC - Laboratoire Hubert Curien

Color constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of these approaches are handcrafted and mostly rely on strong empirical assumptions, e.g., that the average reflectance in a scene is gray. State-of-the-art approaches can perform very well on some given datasets but poorly adapt on some others. In this paper, we have investigated how neural networks-based approaches can be used to deal with the color constancy problem. We have proposed a new network architecture based on existing successful hand-crafted approaches and a large number of improvements to tackle this problem by learning a suitable deep model. We show our results on most of the standard benchmarks used in the color constancy domain.

Mots clés: Color constancy; Neural networks; Light color estimation; Pooling; Data augmentation

Domaines: Apprentissage [cs.LG]

Damien Fourure, Rémi Emonet, Elisa Fromont, Damien Muselet, Alain Trémeau, et al.. Mixed Pooling Neural Networks for Color Constancy. IEEE International Conference on Image Processing, Sep 2016, Phoenix, United States. ⟨hal-01314066⟩