In this work, we investigate how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images. We show how these properties can be used to drive, given a set of illuminant estimation algorithms, the selection of the best algorithm for a given image. The selection is made by a decision forest composed by several trees that vote for one of the illuminant estimation algorithm. The most voted algorithm is then applied to the input image. Experimental results on the widely used Ciurea and Funt dataset demonstrate the accuracy of our approach in comparison to other algorithms in the state of the art. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Bianco, S., Ciocca, G., & Cusano, C. (2009). Color constancy algorithm selection using CART. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5646 LNCS, pp. 31–40). https://doi.org/10.1007/978-3-642-03265-3_4
Mendeley helps you to discover research relevant for your work.