Learning Viewpoint Planning in Active Recognition on a Small Sampling Budget: A Kriging Approach

  • Authors:

  • Joseph Defretin;Julien Marzatz;Helene Piet-Lahanierz

  • Affiliations:

  • -;-;-

  • Venue:
  • ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
  • Year:
  • 2010

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Abstract

This paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy.