Color constancy based on texture pyramid matching and regularized local regression
JOSA A, Vol. 27, Issue 10, pp. 2097-2105 (2010)
http://dx.doi.org/10.1364/JOSAA.27.002097
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Abstract
Considering that no single algorithm available is universal in color constancy, we propose an effective combination approach using a texture-based matching strategy and a local regression with prior-knowledge regularization. To represent the images, we construct a texture pyramid using an integrated Weibull distribution. Then we define an image similarity measure to search for the K most similar images of the test image. To combine the single algorithms, we integrate prior knowledge into a regularized local regression in a decorrelated color space. Regression weights are obtained on these similar images, and the regularization is implemented by the frequency ratio of the best single algorithm. Experiments on two real world datasets show our approach outperforms the state-of-the-art single algorithms and popular combination approaches with a performance increase of at least 29% compared to the best-performing single algorithm w.r.t median angular error.
© 2010 Optical Society of America
OCIS Codes
(150.0150) Machine vision : Machine vision
(330.0330) Vision, color, and visual optics : Vision, color, and visual optics
(330.1690) Vision, color, and visual optics : Color
(330.1720) Vision, color, and visual optics : Color vision
ToC Category:
Machine Vision
History
Original Manuscript: April 5, 2010
Revised Manuscript: August 1, 2010
Manuscript Accepted: August 2, 2010
Published: September 2, 2010
Virtual Issues
Vol. 5, Iss. 14 Virtual Journal for Biomedical Optics
Citation
Meng Wu, Jun Sun, Jun Zhou, and Gengjian Xue, "Color constancy based on texture pyramid matching and regularized local regression," J. Opt. Soc. Am. A 27, 2097-2105 (2010)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-10-2097
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