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Multiple Classifier System Approach to Model Pruning in Object Recognition

  • Josef Kittler
  • Ali R. Ahmadyfard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3024)

Abstract

We propose a multiple classifier system approach to object recognition in computer vision. The aim of the approach is to use multiple experts successively to prune the list of candidate hypotheses that have to be considered for object interpretation. The experts are organised in a serial architecture, with the later stages of the system dealing with a monotonically decreasing number of models. We develop a theoretical model which underpins this approach to object recognition and show how it relates to various heuristic design strategies advocated in the literature. The merits of the advocated approach are then demonstrated experimentally using the SOIL database. We show how the overall performance of a two stage object recognition system, designed using the proposed methodology, improves. The improvement is achieved in spite of using a weak recogniser for the first (pruning) stage. The effects of different pruning strategies are demonstrated.

Keywords

Object Recognition Recognition Rate Pruning Strategy Multiple Expert Candidate Label 
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 2004

Authors and Affiliations

  • Josef Kittler
    • 1
  • Ali R. Ahmadyfard
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUnited Kingdom

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