Advertisement

An Incremental EM Algorithm Based Visual Perceptual Grouping

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes
Chapter

Abstract

With the increasing demand for analytical calculation for datasets of different scales and types, a diversity of clustering algorithms have been developed in recent years, among which the EM clustering algorithm has a good clustering effect and has remained popular in likelihood applications. Based on the superiority of the EM algorithm on clustering small-scale datasets and to further test the performance of the BIRCH tree in classification application, in this chapter, an integration-based fast incremental EM clustering algorithm is presented for multiple-percept detection tasks in image sequences for mobile robotic applications in an unknown environment. Basically, the proposed algorithm first applies the standard EM algorithm to group image-patch-based feature vectors extracted from an image perceptually. Then, a fast tree-based approximate nearest neighbor classifier is employed to integrate the clustering results. Experiments performed in an outdoor environment demonstrate the efficacy of the proposed method.

Keywords

Data clustering EM algorithm Instance-based classification Approximate nearest neighbor search BIRCH tree Image segmentation Perceptual grouping 

References

  1. de Amorim, R. C., & Hennig, C. (2015). Recovering the number of clusters in data sets with noise features using feature rescaling factors. Information Sciences, 324, 126–145.Google Scholar
  2. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, 1–38.Google Scholar
  3. McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.CrossRefGoogle Scholar
  4. Mitchell, T. (1997). Machine learning. McGraw-Hill.Google Scholar
  5. Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan (Ed.), Learning in graphical models (pp. 335–368). Dordrecht: Kluwer.Google Scholar
  6. Ng, S. K., & McLachlan, G. J. (2004a). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37, 1573–1589.CrossRefGoogle Scholar
  7. Ng, S. K., & McLachlan, G. J. (2004b). Using the EM algorithm to train neural networks: Misconceptions and a new algorithm for multiclass classification. IEEE Transactions on Neural Networks, 15, 738–749.CrossRefGoogle Scholar
  8. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65. CrossRefGoogle Scholar
  9. Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Prentice Hall.Google Scholar
  10. Wang, X., Chang, C., & Wang, X. L. (2017, December). A fast incremental spectral clustering algorithm for image segmentation. In Proceedings of The 2017 International Conference on Computational Science and Computational Intelligence, Las Vegas (pp. 15–27).Google Scholar
  11. Wilson, D. R., & Martinez, T. R. (2000). Reduction techniques for instance-based learning algorithms. Machine Learning, 38(3), 257–286.CrossRefGoogle Scholar

Copyright information

© Xi'an Jiaotong University Press 2020

Authors and Affiliations

  • Xiaochun Wang
    • 1
    Email author
  • Xiali Wang
    • 2
  • Don Mitchell Wilkes
    • 3
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Information EngineeringChang’an UniversityXi’anChina
  3. 3.Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

Personalised recommendations