Exemplar-Based Colour Constancy

Exemplar-based learning or, equally, nearest neighbour methods have recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. In computer vision, these types of technique have been successful in several problems such as scene recognition, shape matching, image parsing, character recognition and object detection. Applying the concept of exemplar-based learning to the problem of colour constancy seems odd at first glance since, in the first place, similar nearest neighbour images are not usually affected by precisely similar illuminants and, in the second place, gathering a dataset consisting of all possible real-world images, including indoor and outdoor scenes and for all possible illuminant colours and intensities, is indeed impossible. In this paper we instead focus on surfaces in the image and address the colour constancy problem by unsupervised learning of an appropriate model for each training surface in training images. We find nearest neighbour models for each surface in a test image and estimate its illumination based on comparing the statistics of pixels belonging to nearest neighbour surfaces and the target surface. The final illumination estimation results from combining these estimated illuminants over surfaces to generate a unique estimate. The proposed method has the advantage of overcoming multi-illuminant situations, which is not possible for most current methods. The concept proposed here is a completely new approach to the colour constancy problem. We show that it performs very well, for standard datasets, compared to current colour constancy algorithms.