[1] Wang Ying, Zeng Ping, Luo Xuemei, et al. Low-dimensional multi-spectral space for color reproductionbased on nonnegative constrained principal component analysis [J]. Journal of Southeast University (English Edition), 2009, 25 (4): 486-490. [doi:10.3969/j.issn.1003-7985.2009.04.015]

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Low-dimensional multi-spectral space for color reproductionbased on nonnegative constrained principal component analysis()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 4

Page:

486-490

Research Field:

Information and Communication Engineering

Publishing date:

2009-12-30

Info

Title:

Low-dimensional multi-spectral space for color reproductionbased on nonnegative constrained principal component analysis

Author(s):

Wang Ying¹;  Zeng Ping¹;  2;  Luo Xuemei¹;  Xie Kun¹

¹School of Computer Science and Technology, Xidian University, Xi’an 710071, China
²School of Computer Science, Xi’an Shiyou University, Xi’an 710065, China

Keywords:

spectral color science;  nonnegative constrained principal component analysis;  low-dimensional spectral space;  nonlinear optimization;  multi-spectral images;  spectral reflectance

PACS:

TN911.74;TP301.6

DOI:

10.3969/j.issn.1003-7985.2009.04.015

Abstract:

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis(PCA)to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multi-spectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a low-dimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [0, 1]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

References:

[1] Zhao Yonghui, Berns S R, Taplin A L, et al. An investigation of multispectral imaging for the mapping of pigments in paintings[C]//Proceedings of International Society for Optical and Photonics Engineering. San Jose, CA, USA, 2008, 6810: 681007-1-9.
[2] Bochko V, Tsumura N, Miyake Y. A spectral color imaging system for estimating spectral reflectance of paint[J]. Journal of Imaging Science and Technology, 2007, 51(1): 70-78.
[3] Munzenmayer C, Paulus D, Wittenberg T. A spectral color correction framework for medical applications[J]. IEEE Trans on Biomedical Engineering, 2006, 53(2): 254-265.
[4] Tsummura N, Miyake Y, Imai H F. Medical vision: measurement of skin absolute spectral-reflectance-image and the application to component analysis[C]//Proceedings of the 3rd Int Conf on Multispectral Color Science. Joensuu, Finland, 2001: 25-28.
[5] Wu Chungyi, Lee Shunming, Wen Chaohua, et al. Multi-spectral image acquisition system for color spectrum reproduction[C]//Proceedings of the 16th IPPR Conf on Computer Vision, Graphics and Image Processing. Kinmen, Taiwan, China, 2003: 115-122.
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[7] Yu Shanshan, Murakami Y, Obi T, et al. Multispectral image compression for improvement of colorimetric and spectral reproducibility by nonlinear spectral transform[J]. Optical Review, 2006, 13(2): 346-356.
[8] Yu Shanshan, Murakami Y, Obi T, et al. Improvements for multispectral image compression for color reproducibility with preservation to spectral accuracy[C]//IEEE Int Conf on Image Processing. Genova, Italy, 2005: 710-713.
[9] Derhak W M, Rosen R M. Spectral colorimetry using LabPQR: an interim connection space[J]. Journal of Imaging Science and Technology, 2006, 50(1): 53-63.
[10] Tsutsumi S, Rosen R M, Berns S R. Spectral color management using interim connection spaces based on spectral decomposition[C]//Proceedings of the 14th Color Imaging Conf. Arizona, USA, 2006: 246-251.
[11] Imai H F, Rosen R M, Berns S R. Comparative study of metrics for spectral match quality[C]//Proceedings of the First European Conference on Color Graphics, Imaging, and Vision. Poitiers, France, 2002: 492-496.

Memo

Memo:

Biographies: Wang Ying(1977—), female, graduate; Zeng Ping(corresponding author), male, professor, zp8637@126.com.
Foundation items: The Pre-Research Foundation of National Ministries and Commissions(No.9140A16050109DZ01), the Scientific Research Program of the Education Department of Shanxi Province(No.09JK701).
Citation: Wang Ying, Zeng Ping, Luo Xuemei, et al. Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis[J]. Journal of Southeast University(English Edition), 2009, 25(4): 486-490.

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