Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist
in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive
influence on the performance of the illuminant estimation.
Therefore, in this paper, an extensive analysis is provided
of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented
classifying edge types based on their reflectance properties (e.g. material, shadow-geometry and highlights). Then, a performance
evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation, it
is derived that certain edge types are more valuable than material edges for the estimation of the illuminant. To this end,
the weighted Grey-Edge algorithm is proposed in which certain valuable edge types are more emphasized for the estimation of
From the experimental results, it is shown that the proposed weighted Grey-Edge algorithm based on the
shadow-shading variant, i.e. assigning higher weights to edges with more energy in the shadow-shading direction, results in
the best performance. Moreover, all current state-of-the-art methods, including pixel-based methods and edge-based methods,
have been significantly outperformed by the proposed weighted Grey-Edge algorithm, resulting in an improvement of 9% over
the current best-performing algorithm.