A Graph-Based Color Lines Model for Image Analysis

  • D. Duque-AriasEmail author
  • S. Velasco-Forero
  • J.-E. Deschaud
  • F. Goulette
  • B. Marcotegui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11752)


This paper addresses the problem of obtaining a concise description of spectral representation for color images. The proposed method is a graph-based formulation of the well-known Color Lines model. It generalizes the lines to piece-wise lines, been able to fit more complex structures. We illustrate the goodness of proposed method by measuring the quality of the simplified representations in images and videos. The quality of video sequences reconstructed by means of proposed color lines extracted from the first frame demonstrates the robustness of our representation. Our formalism allows to address applications such as image segmentation, shadow correction among others.


Color lines Gaussian mixture model Minimum Spanning Tree Spectral representation Graph-based modeling 


  1. 1.
    Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Heidelberg (2013). Scholar
  2. 2.
    Permuter, H., Francos, J., Jermyn, I.: A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recogn. 39(4), 695–706 (2006)CrossRefGoogle Scholar
  3. 3.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 72(2), 195–215 (2007)CrossRefGoogle Scholar
  4. 4.
    Gouiffès, M., Zavidovique, B.: Body color sets: a compact and reliable representation of images. J. Vis. Commun. Image Represent. 22(1), 48–60 (2011)CrossRefGoogle Scholar
  5. 5.
    Omer, I., Werman, M.: Color lines: image specific color representation. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II. IEEE (2004)Google Scholar
  6. 6.
    Aksoy, Y., Aydın, T.O., Smolić, A., Pollefeys, M.: Unmixing-based soft color segmentation for image manipulation. ACM Trans. Graph. 36(2), 19:1–19:19 (2017)CrossRefGoogle Scholar
  7. 7.
    Angulo, J.: Morphological colour operators in totally ordered lattices based on distances: application to image filtering, enhancement and analysis. Comput. Vis. Image Underst. 107(1–2), 56–73 (2007)CrossRefGoogle Scholar
  8. 8.
    Nishikawa, T., Tanaka, Y.: Dynamic color lines. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2247–2251, October 2018Google Scholar
  9. 9.
    Yu, X., Li, G., Ying, Z., Guo, X.: A new shadow removal method using color-lines. In: Felsberg, M., Heyden, A., Krüger, N. (eds.) CAIP 2017. LNCS, vol. 10425, pp. 307–319. Springer, Cham (2017). Scholar
  10. 10.
    Fattal, R.: Dehazing using color-lines, vol. 34, pp. 1–14. ACM, New York (2014)CrossRefGoogle Scholar
  11. 11.
    Buades, A., Lisani, J.L., Morel, J.-M.: On the distribution of colors in natural images (2010)Google Scholar
  12. 12.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  13. 13.
    Sreevani, Murthy, C.A.: On bandwidth selection using minimal spanning tree for kernel density estimation. Comput. Stat. Data Anal. 102, 67–84 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yu, Z., Au, O.C., Tang, K., Xu, C.: Nonparametric density estimation on a graph: learning framework, fast approximation and application in image segmentation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2201–2208. IEEE (2011)Google Scholar
  15. 15.
    Kawaguchi, K., Kaelbling, L.P., Bengio, Y.: Generalization in deep learning. arXiv preprint arXiv:1710.05468 (2017)
  16. 16.
    Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Computer Vision and Pattern Recognition (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • D. Duque-Arias
    • 1
    Email author
  • S. Velasco-Forero
    • 1
  • J.-E. Deschaud
    • 2
  • F. Goulette
    • 2
  • B. Marcotegui
    • 1
  1. 1.MINES ParisTech, CMM-Center of Mathematical MorphologyPSL Research UniversityParisFrance
  2. 2.MINES ParisTech, CAOR-Centre de RobotiquePSL Research UniversityParisFrance

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