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 algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features. The features represent the image content in terms of low-level visual properties. The trees are trained to select the algorithm that minimizes the expected error in illuminant estimation. We also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms' estimations. Experimental results on the widely used Ciurea and Funt dataset demonstrate the effectiveness of our approach.

Bianco, S., Ciocca, G., Cusano, C., Schettini, R. (2010). Automatic Color Constancy Algorithm Selection and Combination. PATTERN RECOGNITION, 43(3), 695-705 [10.1016/j.patcog.2009.08.007].

Automatic Color Constancy Algorithm Selection and Combination

BIANCO, SIMONE;CIOCCA, GIANLUIGI;SCHETTINI, RAIMONDO
2010

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 algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features. The features represent the image content in terms of low-level visual properties. The trees are trained to select the algorithm that minimizes the expected error in illuminant estimation. We also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms' estimations. Experimental results on the widely used Ciurea and Funt dataset demonstrate the effectiveness of our approach.
Articolo in rivista - Articolo scientifico
Color constancy, Image indexing, Classification, Decision forests
English
695
705
11
Bianco, S., Ciocca, G., Cusano, C., Schettini, R. (2010). Automatic Color Constancy Algorithm Selection and Combination. PATTERN RECOGNITION, 43(3), 695-705 [10.1016/j.patcog.2009.08.007].
Bianco, S; Ciocca, G; Cusano, C; Schettini, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/27376
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