Applying visual object categorization and memory colors for automatic color constancy

Abstract. This paper presents a framework for using high-level visual information to enhance the performance of automatic color constancy algorithms. The approach is based on recognizing special visual object categories, called here as memory color categories, which have a relatively constant color (e.g. the sky). If such category is found from image, the initial white balance provided by a low-level color constancy algorithm can be adjusted so that the observed color of the category moves toward the desired color. The magnitude and direction of the adjustment is con-trolled by the learned characteristics of the particular category in the chromaticity space. The object categorization is performed using bag-of-features method and raw camera data with reduced preprocessing and resolution. The proposed approach is demonstrated in experiments in-volving the standard gray-world and the state-of-the-art gray-edge color constancy methods. In both cases the introduced approach improves the performance of the original methods