The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Investigation of color constancy with a neural network
Neural Networks
A Six-Stimulus Theory for Stochastic Texture
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Gamut Constrained Illuminant Estimation
International Journal of Computer Vision
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Constancy
Automatic color constancy algorithm selection and combination
Pattern Recognition
Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
Color Constancy Using Natural Image Statistics and Scene Semantics
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Design and Analysis of the Privacy-Preserving SVM Classifier
IEEE Transactions on Knowledge and Data Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Adaptive Spectral Transform for Wavelet-Based Color Image Compression
IEEE Transactions on Circuits and Systems for Video Technology
Computational Color Constancy: Survey and Experiments
IEEE Transactions on Image Processing
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This paper presents a new combining approach for color constancy, the problem of finding the true color of objects independent of the light illuminating the scene. There are various combining methods in the literature that all of them use weighting approach with either pre-determined static weights for all images or dynamically computed weights for each image. The problem with weighting approach is that due to the inherent characteristics of color constancy methods, finding suitable weights for combination is a difficult and error-prone task. In this paper, a new optimization based combining method is proposed which does not need explicit weight assignment. The proposed method has two phases: first, the best group of color constancy algorithms for the given image is determined and then, some of the algorithms in this group are combined using multi-objective optimization methods. To the best of our knowledge, this is the first time that optimization methods are used in color constancy problem. The proposed method has been evaluated using two benchmark datasets and the experimental results were satisfactory in compare with state of the art algorithms.