Evaluation of Color Spaces for Robust Image Segmentation

Alexander Jungmann, Jan Jatzkowski, Bernd Kleinjohann


In this paper, we evaluate the robustness of our color-based segmentation approach in combination with different color spaces, namely RGB, L*a*b*, HSV, and log-chromaticity (LCCS). For this purpose, we describe our deterministic segmentation algorithm including its gradually transformation of pixel-precise image data into a less error-prone and therefore more robust statistical representation in terms of moments. To investigate the robustness of a specific segmentation setting, we introduce our evaluation framework that directly works on the statistical representation. It is based on two different types of robustness measures, namely relative and absolute robustness. While relative robustness measures stability of segmentation results over time, absolute robustness measures stability regarding varying illumination by comparing results with ground truth data. The significance of these robustness measures is shown by evaluating our segmentation approach with different color spaces. For the evaluation process, an artificial scene was chosen as representative for application scenarios based on artificial landmarks.


  1. Blas, M., Agrawal, M., Sundaresan, A., and Konolige, K. (2008). Fast color/texture segmentation for outdoor robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4078- 4085.
  2. Busin, L., Shi, J., Vandenbroucke, N., and Macaire, L. (2009). Color space selection for color image segmentation by spectral clustering. In IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pages 262-267.
  3. Finlayson, G. D., Drew, M. S., and Lu, C. (2009). Entropy minimization for shadow removal. Int. J. Comput. Vision, 85(1):35-57.
  4. Freixenet, J., Mun˜ oz, X., Raba, D., Martí, J., and Cufí, X. (2002). Yet another survey on image segmentation: Region and boundary information integration. In Proceedings of the 7th European Conference on Computer Vision (ECCV), pages 408-422.
  5. Hu, M.-K. (1962). Visual Pattern Recognition by Moment Invariants. IEEE Transactions on Information Theory, 8(2):179-187.
  6. Jungmann, A., Kleinjohann, B., Kleinjohann, L., and Bieshaar, M. (2012). Efficient color-based image segmentation and feature classification for image processing in embedded systems. In Proceedings of the 4th International Conference on Resource Intensive Applications and Services (INTENSIVE), pages 22- 29.
  7. Jungmann, A., Stern, C., Kleinjohann, L., and Kleinjohann, B. (2010). Increasing motion information by using universal tracking of 2d-features. In Proceedings of the 8th IEEE International Conference on Industrial Informatics (INDIN), pages 511-516.
  8. Khanal, B., Ali, S., and Sidib, D. (2012). Robust road signs segmentation in color images. In Proceedings of the 7th International Conference on Computer Vision Theory and Applications (VISAPP), pages 307-310.
  9. Mogelmose, A., Trivedi, M., and Moeslund, T. (2012). Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey. IEEE Transactions on Intelligent Transportation Systems, 13(4):1484-1497.
  10. Russell, S. and Norvig, P. (2010). Artificial Intelligence - A Modern Approach. Pearson, 3 edition.
  11. Szeliski, R. (2011). Computer Vision - Algorithms a. Applications. Springer.
  12. Unnikrishnan, R., Pantofaru, C., and Hebert, M. (2007). Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):929-944.
  13. Vandenbroucke, N., Macaire, L., and Postaire, J.-G. (2003). Color image segmentation by pixel classification in an adapted hybrid color space: application to soccer image analysis. Computer Vision and Image Understanding, 90(2):190-216.
  14. Yang, U., Kim, B., and Sohn, K. (2009). Illumination invariant skin color segmentation. In 4th IEEE Conference on Industrial Electronics and Applications (ICIEA), pages 636-641.

Paper Citation

in Harvard Style

Jungmann A., Jatzkowski J. and Kleinjohann B. (2014). Evaluation of Color Spaces for Robust Image Segmentation . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 648-655. DOI: 10.5220/0004743406480655

in Bibtex Style

author={Alexander Jungmann and Jan Jatzkowski and Bernd Kleinjohann},
title={Evaluation of Color Spaces for Robust Image Segmentation},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Evaluation of Color Spaces for Robust Image Segmentation
SN - 978-989-758-003-1
AU - Jungmann A.
AU - Jatzkowski J.
AU - Kleinjohann B.
PY - 2014
SP - 648
EP - 655
DO - 10.5220/0004743406480655