Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions

Color pixel distributions provide a useful cue for object recognition but are dependent on scene illumination. We develop an algorithm that assigns color descriptors to an object that depend on the surface properties of the object and not on the illumination. An object is defined by a set of possibly textured surfaces and gives rise to a color pixel distribution. For a trichromatic system, the algorithm assumes a three-dimensional linear model for surface spectral reflectance. There are no assumptions about the contents of the scene and only weak constraints on the illumination. The global color invariants can be computed in an amount of time that is proportional to the number of pixels that define an object. A set of experiments on complex scenes under various illuminants demonstrates that the global color constancy algorithm performs significantly better than previous recognition algorithms based on color distribution.