International Journal of Computer Vision
Machine Learning
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Pattern Search Methods for Linearly Constrained Minimization
SIAM Journal on Optimization
Is Machine Colour Constancy Good Enough?
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Re-evaluating Colour Constancy Algorithms
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Combined Physical and Statistical Approach to Colour Constancy
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Supervised and unsupervised classification post-processing for visual video summaries
IEEE Transactions on Consumer Electronics
On the selection of an optimal wavelet basis for texture characterization
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Improving Color Constancy Using Indoor–Outdoor Image Classification
IEEE Transactions on Image Processing
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
An integrated framework for biometrics security
iUBICOM'10 Proceedings of the 5th international conference on Ubiquitous and Collaborative Computing
Face-based illuminant estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Color constancy using single colors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A linear system form solution to compute the local space average color
Machine Vision and Applications
Multi-objective optimization based color constancy
Applied Soft Computing
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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.