An Incremental EM Algorithm Based Visual Perceptual Grouping

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes


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.


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


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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

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