Adaptive Motion Segmentation Algorithm Based on the Principal Angles Configuration

  • L. Zappella
  • E. Provenzi
  • X. Lladó
  • J. Salvi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Many motion segmentation algorithms based on manifold clustering rely on a accurate rank estimation of the trajectory matrix and on a meaningful affinity measure between the estimated manifolds. While it is known that rank estimation is a difficult task, we also point out the problems that can be induced by an affinity measure that neglects the distribution of the principal angles. In this paper we suggest a new interpretation of the rank of the trajectory matrix and a new affinity measure. The rank estimation is performed by analysing which rank leads to a configuration where small and large angles are best separated. The affinity measure is a new function automatically parametrized so that it is able to adapt to the actual configuration of the principal angles. Our technique has one of lowest misclassification rates on the Hopkins155 database and has good performances also on synthetic sequences with up to 5 motions and variable noise level.


Singular Value Decomposition Rank Estimation Motion Segmentation Synthetic Sequence Affinity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • L. Zappella
    • 1
  • E. Provenzi
    • 2
  • X. Lladó
    • 1
  • J. Salvi
    • 1
  1. 1.Institut d’Informàtica i AplicacionsUniversitat de GironaGironaSpain
  2. 2.Departamento de Tecnologías de la Información y las ComunicacionesUniversitat Pompeu FabraBarcelonaSpain

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