Region-Based Illuminant Estimation for Effective Color Correction
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Automatic color constancy algorithm selection and combination
Pattern Recognition
Color constancy using stage classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Novel Method for Efficient Indoor---Outdoor Image Classification
Journal of Signal Processing Systems
Perceptually motivated automatic color contrast enhancement based on color constancy estimation
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Real-time detection of landscape scenes
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Automatic color detection of archaeological pottery with munsell system
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Exploiting depth information for indoor-outdoor scene classification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Pixel distribution shifting color correction for digital color images
Applied Soft Computing
Instant scene recognition on mobile platform
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
Simultaneous image color correction and enhancement using particle swarm optimization
Engineering Applications of Artificial Intelligence
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In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough. The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters. We have tested the proposed strategies on a suitable subset of the widely used Funt and Ciurea dataset. Experimental results clearly demonstrate that classification based strategies outperform general purpose algorithms.