Color Constancy with Derivative Colors
This addresses color constancy for computer vision applications, but it is incremental as it builds on existing dichromatic reflection models.
The paper tackled color constancy by using derivative colors from achromatic and highlight regions to compute illuminant color robustly with kernel density estimation, achieving satisfactory performance in experiments on three standard databases compared to state-of-the-art methods.
Information about the illuminant color is well contained in both achromatic regions and the specular components of highlight regions. In this paper, we propose a novel way to achieve color constancy by exploiting such clues. The key to our approach lies in the use of suitably extracted derivative colors, which are able to compute the illuminant color robustly with kernel density estimation. While extracting derivative colors from achromatic regions to approximate the illuminant color well is basically straightforward, the success of our extraction in highlight regions is attributed to the different rates of variation of the diffuse and specular magnitudes in the dichromatic reflection model. The proposed approach requires no training phase and is simple to implement. More significantly, it performs quite satisfactorily under inter-database parameter settings. Our experiments on three standard databases demonstrate its effectiveness and fine performance in comparison to state-of-the-art methods.