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Random Convolution Ensembles

  • Michael Mayo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4810)

Abstract

A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective.

Keywords

Image Classification Random Convolution Pedestrian Detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Mayo
    • 1
  1. 1.Dept. of Computer Science, University of Waikato, Private Bag 3105, HamiltonNew Zealand

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