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Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm


We propose a method and a tool for automatic generation of hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in terms of FPGA's slice, using different weak classifiers based on the general concept of hyperrectangle. The main novelty of our approach is that the tool allows the user to find automatically an appropriate tradeoff between classification performances and hardware implementation cost, and that the generated architecture is optimized for each training process. We present results obtained using Gaussian distributions and examples from UCI databases. Finally, we present an example of industrial application of real-time textured image segmentation.

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Correspondence to J. Mitéran.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mitéran, J., Matas, J., Bourennane, E. et al. Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm. EURASIP J. Adv. Signal Process. 2005, 198439 (2005).

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Keywords and phrases:

  • Adaboost
  • FPGA
  • classification
  • hardware
  • image segmentation