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.
Color Constancy Algorithm Selection Using CART
CUSANO, CLAUDIO;
2009-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
