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Accelerating the boosting approach to training classifiers

  • US 7,421,114 B1
  • Filed: 11/22/2004
  • Issued: 09/02/2008
  • Est. Priority Date: 11/22/2004
  • Status: Active Grant
First Claim
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1. A method for training a classifier, the method comprising:

  • receiving a training set that includes images that correspond to an object of interest (positive images) and images that do not correspond to an object of interest (negative images);

    receiving a restricted set of linear operators; and

    using a boosting process to train a classifier to discriminate between the positive and negative images in the training set, the classifier being an aggregate of multiple individual classifiers, the boosting process being an iterative process, the iterations including;

    a first iteration where an individual classifier in the aggregate is trained by;

    (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set;

    (2) selecting for use by the individual classifier the linear operator with the lowest error rate (error-minimizing operator); and

    (3) generating a re-weighted version of the training set that is weighted such that images that were misclassified by the error-minimizing operator are weighted more than images that were classified correctly by the error-minimizing operator; and

    subsequent iterations during which another individual classifier in the aggregate is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.

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