Color Constancy with Derivative Colors

TL;DR

This paper introduces a novel color constancy method that uses derivative colors from achromatic and highlight regions, leveraging kernel density estimation for robust illuminant color computation without training.

Contribution

It proposes a new approach using derivative colors and kernel density estimation for color constancy, effective across different databases without training.

Findings

Effective in diverse datasets

No training required

Outperforms state-of-the-art methods

Abstract

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.