A supervised combination strategy for illumination chromaticity estimation

Authors:
Bing Li;Weihua Xiong;De Xu;Hong Bao
Affiliations:
Institute of Automation, Chinese Academy of Sciences and Beijing Jiaotong University, Beijing, China;OmniVision Technologies, Sunnyvale, CA;Beijing Jiaotong University, Beijing, China;Beijing Jiaotong University, Beijing, China
Venue:
ACM Transactions on Applied Perception (TAP)
Year:
2010

Citing 12
Cited 0

Color and Illuminant Voting

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
Colour by Correlation in a Three-Dimensional Colour Space

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Separating illumination from reflectance in colour imagery

Separating illumination from reflectance in colour imagery
Color Constancy

Color Constancy
Letters: Fully complex extreme learning machine

Neurocomputing
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

IEEE Transactions on Information Theory
A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data

IEEE Transactions on Image Processing
A comparison of computational color constancy Algorithms. II. Experiments with image data

IEEE Transactions on Image Processing
Edge-Based Color Constancy

IEEE Transactions on Image Processing
Learning capability and storage capacity of two-hidden-layer feedforward networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

Color constancy is an important perceptual ability of humans to recover the color of objects invariant of light information. It is also necessary for a robust machine vision system. Until now, a number of color constancy algorithms have been proposed in the literature. In particular, the edge-based color constancy uses the edge of an image to estimate light color. It is shown to be a rich framework that can represent many existing illumination estimation solutions with various parameter settings. However, color constancy is an ill-posed problem; every algorithm is always given out under some assumptions and can only produce the best performance when these assumptions are satisfied. In this article, we have investigated a combination strategy relying on the Extreme Learning Machine (ELM) technique that integrates the output of edge-based color constancy with multiple parameters. Experiments on real image data sets show that the proposed method works better than most single-color constancy methods and even some current state-of-the-art color constancy combination strategies.