Making a Shallow Network Deep: Conversion of a Boosting Classifier into a Decision Tree by Boolean Optimisation

  • Authors:

  • Tae-Kyun Kim;Ignas Budvytis;Roberto Cipolla

  • Affiliations:

  • Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK SW7 2AZ;Department of Engineering, University of Cambridge, Cambridge, UK CB2 1PZ;Department of Engineering, University of Cambridge, Cambridge, UK CB2 1PZ

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2012

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Abstract

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimisation problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit--a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems.