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Automatic Motion Segmentation via a Cumulative Kernel Representation and Spectral Clustering

  • O. R. Oña-Rocha
  • O. T. Sánchez-Manosalvas
  • A. C. Umaquinga-Criollo
  • P. D. Rosero-Montalvo
  • L. E. Suárez-Zambrano
  • J. L. Rodríguez-Sotelo
  • D. H. Peluffo-Ordóñez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10585)

Abstract

Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.

Keywords

Kernel spectral clustering Motion segmentation Time-varying data Variable ranking 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • O. R. Oña-Rocha
    • 1
    • 2
  • O. T. Sánchez-Manosalvas
    • 2
  • A. C. Umaquinga-Criollo
    • 1
  • P. D. Rosero-Montalvo
    • 1
    • 4
  • L. E. Suárez-Zambrano
    • 1
  • J. L. Rodríguez-Sotelo
    • 5
  • D. H. Peluffo-Ordóñez
    • 1
    • 3
    • 6
  1. 1.Universidad Técnica del NorteIbarraEcuador
  2. 2.Universidad de las Fuerzas Armadas - ESPESangolquíEcuador
  3. 3.Corporación Universitaria Autónoma de NariñoPastoColombia
  4. 4.Instituto Tecnológico Superior 17 de JulioYachayEcuador
  5. 5.Universidad Autónoma de ManizalesManizalesColombia
  6. 6.Universidad de NariñoPastoColombia

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