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Object Detection using Fuzzy Adaboost

퍼지 Adaboost를 이용한 객체 검출

  • Received : 2016.03.18
  • Accepted : 2016.04.21
  • Published : 2016.05.28

Abstract

The Adaboost chooses a good set of features in rounds. On each round, it chooses the optimal feature and its threshold value by minimizing the weighted error of classification. The involved process of classification performs a hard decision. In this paper, we expand the process of classification to a soft fuzzy decision. We believe this expansion could allow some flexibility to the Adaboost algorithm as well as a good performance especially when the size of a training data set is not large enough. The typical Adaboost algorithm assigns a same weight to each training datum on the first round of a training process. We propose a new algorithm to assign different initial weights based on some statistical properties of involved features. In experimental results, we assess that the proposed method shows higher performance than the traditional one.

Keywords

Adaboost;Fuzzy Inference;Data Distribution;Object Detection

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Acknowledgement

Supported by : 한국연구재단