Changing illumination cause the measurements of object colors to be biased toward chromaticity of illuminants. Various color constancy algorithms are already exist to remove the chromaticity of illuminants in an image for improving image quality. Recently, NMFsc(nonnegative matrix factorization with sparseness constraint) was introduced to extract the illuminant and reflectance component in an image. NMFsc extract illuminant component and reflectance component by using nonnegative matrix decomposition and sparseness constraints. However, if an image has a chromaticity distribution dominated by a particular chromaticity, the sparse constraint values include that dominant chromaticity, thereby inducing color distortion. Therefore, the proposed method modified the matrix decomposition in NMFsc by using standard deviation and K-means algorithm in chromaticity space. Next, non-negative matrix decomposition and sparseness constraints are performed on an image. Subsequently, illumination is estimated by combining the low sparse constraint values that excludes the dominant chromaticity. The performance of the proposed method is evaluated by using angular error for Ciurea 11,346 image data set. Experimental results illustrate that the proposed method reduces the angular error over previous methods.

Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9395, Color Imaging XX: Displaying, Processing, Hardcopy, and Applications, 93950V (8 February 2015); doi: 10.1117/12.2078968

Published in SPIE Proceedings Vol. 9395:
Color Imaging XX: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Alessandro Rizzi, Editor(s)