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http://dx.doi.org/10.5573/ieie.2015.52.8.089

Illumination Estimation Based on Nonnegative Matrix Factorization with Dominant Chromaticity Analysis  

Lee, Ji-Heon (School of Electronics Engineering, Kyungpook National University)
Kim, Dae-Chul (School of Electronics Engineering, Kyungpook National University)
Ha, Yeong-Ho (School of Electronics Engineering, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.8, 2015 , pp. 89-96 More about this Journal
Abstract
Human visual system has chromatic adaptation to determine the color of an object regardless of illumination, whereas digital camera records illumination and reflectance together, giving the color appearance of the scene varied under different illumination. NMFsc(nonnegative matrix factorization with sparseness constraint) was recently introduced to estimate original object color by using sparseness constraint. In NMFsc, low sparseness constraint is used to estimate illumination and high sparseness constraint is used to estimate reflectance. However, NMFsc has an illumination estimation error for images with large uniform area, which is considered as dominant chromaticity. To overcome the defects of NMFsc, illumination estimation via nonnegative matrix factorization with dominant chromaticity image is proposed. First, image is converted to chromaticity color space and analyzed by chromaticity histogram. Chromaticity histogram segments the original image into similar chromaticity images. A segmented region with the lowest standard deviation is determined as dominant chromaticity region. Next, dominant chromaticity is removed in the original image. Then, illumination estimation using nonnegative matrix factorization is performed on the image without dominant chromaticity. To evaluate the proposed method, experimental results are analyzed by average angular error in the real world dataset and it has shown that the proposed method with 5.5 average angular error achieve better illuminant estimation over the previous method with 5.7 average angular error.
Keywords
illumination estimation; nonnegative matrix factorization; dominant chromaticity analysis;
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