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Multiple Structure Recovery via Probabilistic Biclustering

  • M. DenittoEmail author
  • L. Magri
  • A. Farinelli
  • A. Fusiello
  • M. Bicego
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

Multiple Structure Recovery (MSR) represents an important and challenging problem in the field of Computer Vision and Pattern Recognition. Recent approaches to MSR advocate the use of clustering techniques. In this paper we propose an alternative method which investigates the usage of biclustering in MSR scenario. The main idea behind the use of biclustering approaches to MSR is to isolate subsets of points that behave “coherently” in a subset of models/structures. Specifically, we adopt a recent generative biclustering algorithm and we test the approach on a widely accepted MSR benchmark. The results show that biclustering techniques favorably compares with state-of-the-art clustering methods.

Keywords

Subspace Cluster Fundamental Matrice Preference Matrix Motion Segmentation Feature Trajectory 
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 International Publishing AG 2016

Authors and Affiliations

  • M. Denitto
    • 1
    Email author
  • L. Magri
    • 1
  • A. Farinelli
    • 1
  • A. Fusiello
    • 2
  • M. Bicego
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.DPIAUniversity of UdineUdineItaly

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