Image Segmentation Based on Multi-Kernel Learning and Feature Relevance Analysis

  • S. Molina-Giraldo
  • A. M. Álvarez-Meza
  • D. H. Peluffo-Ordoñez
  • G. Castellanos-Domínguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7637)


In this paper an automatic image segmentation methodology based on Multiple Kernel Learning (MKL) is proposed. In this regard, we compute some image features for each input pixel, and then combine such features by means of a MKL framework. We automatically fix the weights of the MKL approach based on a relevance analysis over the original input feature space. Moreover, an unsupervised image segmentation measure is used as a tool to establish the employed kernel free parameter. A Kernel Kmeans algorithm is used as spectral clustering method to segment a given image. Experiments are carried out aiming to test the efficiency of the incorporation of weighted feature information into clustering procedure, and to compare the performance against state of the art algorithms, using a supervised image segmentation measure. Attained results show that our approach is able to compute a meaningful segmentations, demonstrating its capability to support further vision computer applications.


kernel learning spectral clustering relevance analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ozyildiz, E., Krahnstöver, N., Sharma, R.: Adaptive Texture and Color Segmentation for Tracking Moving Objects. Pattern Recognition 35(10), 2013–2029 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Besl, P., Jain, R.: Three-Dimensional Object Recognition. ACM Computing Surveys 17(1), 75–145 (1985)CrossRefGoogle Scholar
  3. 3.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Daza-Santacoloma, G., Arias-Londoño, J.D., Godino-Llorente, J.I., Sáenz-Lechón, N., Osma-Ruíz, V., Castellanos-Dom’inguez, G.: Dynamic Feature Extraction – An Application to Voice Pathology Detection. Intelligent Automation and Soft Computing 15(4), 667–682 (2009)Google Scholar
  6. 6.
    Dhillon, I., Guan, Y., Kulis, B.: Kernel K-Means – Spectral Clustering and Normalized Cuts. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 551–556. ACM (2004)Google Scholar
  7. 7.
    Gonen, M., Alpaydin, E.: Localized Multiple Kernel Regression. In: 20th International Conference on Pattern Recognition (ICPR 2010), pp. 1425–1428. IEEE (2010)Google Scholar
  8. 8.
    Jung, C., Jiao, L., Liu, J., Shen, Y.: Image Segmentation Via Manifold Spectral Clustering. In: 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), pp. 1–6. IEEE (2011)Google Scholar
  9. 9.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: 8th IEEE International Conference on Computer Vision (ICCV 2001), vol. 2, pp. 416–423. IEEE (2001)Google Scholar
  10. 10.
    Pantofaru, C., Hebert, M.: A Comparison of Image Segmentation Algorithms. Tech. Rep. 336, Robotics Institute (2005)Google Scholar
  11. 11.
    Perona, P., Zelnik-Manor, L.: Self-Tuning Spectral Clustering. Advances in Neural Information Processing Systems 17, 1601–1608 (2004)Google Scholar
  12. 12.
    Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: SimpleMKL. Journal of Machine Learning Research 9, 2491–2521 (2008)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Rosenberger, C., Chehdi, K.: Genetic Fusion – Application to Multi-Components Image Segmentation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2000), vol. 6, pp. 2223–2226. IEEE (2000)Google Scholar
  14. 14.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: A Measure for Objective Evaluation of Image Segmentation Algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition – Workshops (CVPR 2005 Workshops), pp. 34:1–34:8 (2005)Google Scholar
  15. 15.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward Objective Evaluation of Image Segmentation Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 929–944 (2007)CrossRefGoogle Scholar
  16. 16.
    Wang, D.: Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 539–546 (1998)CrossRefGoogle Scholar
  17. 17.
    Zhang, H., Fritts, J., Goldman, S.: Image Segmentation Evaluation – A Survey of Unsupervised Methods. Computer Vision and Image Understanding 110(2), 260–280 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • S. Molina-Giraldo
    • 1
  • A. M. Álvarez-Meza
    • 1
  • D. H. Peluffo-Ordoñez
    • 1
  • G. Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

Personalised recommendations