An Unsupervised Nonparametric and Cooperative Approach for Classification of Multicomponent Image Contents

Akar Taher, Kacem Chehdi, Claude Cariou

Abstract

In this paper an unsupervised nonparametric cooperative and adaptive approach for multicomponent image partitioning is presented. In this approach the images are partitioned component by component and intermediate classification results are evaluated and fused, to get the final partitioning result. Two unsupervised classification methods are used in parallel cooperation to partition each component of the image. The originality of the approach relies i) on its local adaptation to the type of regions in an image (textured, non-textured), ii) on the automatic estimation of the number of classes and iii) on the introduction of several levels of evaluation and validation of intermediate partitioning results before obtaining the final classification result. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each component and its adjacent components, and finally the information of all the components. In our approach, the detected region types are treated separately from the feature extraction step, to the final classification results. The efficiency of our approach is shown on two real applications using a hyperspectral image for the identification of invasive and non invasive vegetation and a multispectral image for pine trees detection.

References

  1. Benediktsson J. A., Kanellopoulos I., 1999. Classification of multisource and hyperspectral data based on decision fusion. IEEE Trans. on Geoscience and Remote Sensing. 37(3), 1367-1377.
  2. Bezdek J. C., 1981. Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers Norwell, MA, USA.
  3. Brodatz P., 1966. Textures: A Photographic Album for Artists and Designers, Dover Publications, New York.
  4. Cox D. R. and Snell E. J., 1968. A general definition of residuals. Journal of the Royal Statistical Society, Series B. 30(2), 248-275.
  5. Dempster A. P., N. M. Laird and D. B. Rubin, 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B. 39(1), 1-38.
  6. Forestier G., Gancarski P., and Wemmert C., 2010. Collaborative clustering with background knowledge. Data and Knowledge Engineering. 69(2), 211-228.
  7. Frey B. J. and Dueck D., 2007. Clustering by passing messages between data points. University of Toronto Science 315, 972-976.
  8. Haralick R., 1979. Statistical and structural approaches to texture. Proceedings of the IEEE. 69, 786-804.
  9. Havens T. C., Bezdek J. C., Leckie Ch., Lawrence O., and Palaniswami M., 2012. Fuzzy C-means algorithms for very large data. IEEE Trans. on Fuzzy Systems. 20(6), 1130-1146.
  10. Holland J. H., 1992. Adaptation in natural and artificial system. 2nd Edition. Cambridge, MA, USA: MIT press.
  11. Huang B., Xie L., 2010. An improved LBG algorithm for image vector quantization. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT).
  12. Kalluri H. R., Prasad S., and Bruce L. M., 2010. Decisionlevel fusion of spectral reflectance and derivative information for robust hyperspectral land cover classification. IEEE Trans. on Geoscience and Remote Sensing. 48(11), 4047-4057.
  13. Kermad Ch., Chehdi K., 2002. Research of an automatic and unsupervised system of segmentation. Image and Vision Computing Edition Elsevier. 541-555.
  14. Kermad Ch, Chehdi K, Cariou C, 1995. Segmentation d'images par multi-seuillage et fusion des régions labellisée minimisant un critère de similarité. In Quinzième Colloque GRETSI, 2, 641-644. Juan-LesPins, France.
  15. Linde, Y., Buzo, A., Gray R. M., 1980. An Algorithm for vector quantizer design. IEEE Transactions on Communications. 28, 84-94.
  16. McQueen J., 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. 1, 281-297.
  17. Neumann F., Oliveto P. S., Witt C., 2009. Heoretical analysis of fitness-proportional selection: landscapes and efficiency. In Proc. of 11th (GECCO), 835-842.
  18. Rosenberger Ch., and Chehdi K., 2003. Unsupervised segmentation of multi-spectral images. In Proc. International Conference on Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium.
  19. Syswerda G., 1989. Uniform crossover in genetic algorithms. In Proc. 3rd International Conference on Genetic Algorithms, 2-9.
  20. Tarabalka Y., Benediktsson J. A., and Chanussot J., 2009. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. on Geoscience and Remote Sensing. 47(8), 2973-2987.
  21. Vapnik V., 1998. A tutorial on support vector machines for pattern recognition. Statistical Learning Theory, New York: Wiley, 1998. C. Burges, Data Mining and Knowledge Discovery. 2, 121-167.
  22. Xu R., Wunsch II D., 2005. Survey of clustering algorithms. IEEE Trans. on Neural Networks. 16(3), 645-678.
Download


Paper Citation


in Harvard Style

Taher A., Chehdi K. and Cariou C. (2014). An Unsupervised Nonparametric and Cooperative Approach for Classification of Multicomponent Image Contents . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 263-270. DOI: 10.5220/0004828502630270


in Bibtex Style

@conference{icpram14,
author={Akar Taher and Kacem Chehdi and Claude Cariou},
title={An Unsupervised Nonparametric and Cooperative Approach for Classification of Multicomponent Image Contents},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={263-270},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004828502630270},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Unsupervised Nonparametric and Cooperative Approach for Classification of Multicomponent Image Contents
SN - 978-989-758-018-5
AU - Taher A.
AU - Chehdi K.
AU - Cariou C.
PY - 2014
SP - 263
EP - 270
DO - 10.5220/0004828502630270