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Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree

다중 분포 학습 모델을 위한 Haar-like Feature와 Decision Tree를 이용한 학습 알고리즘

  • 곽주현 (건국대학교 컴퓨터공학과) ;
  • 원일용 (호서서울전문학교 사이버해킹보안과) ;
  • 이창훈 (건국대학교 컴퓨터공학과)
  • Received : 2012.08.01
  • Accepted : 2012.10.09
  • Published : 2013.01.31

Abstract

Adaboost is widely used for Haar-like feature boosting algorithm in Face Detection. It shows very effective performance on single distribution model. But when detecting front and side face images at same time, Adaboost shows it's limitation on multiple distribution data because it uses linear combination of basic classifier. This paper suggest the HDCT, modified decision tree algorithm for Haar-like features. We still tested the performance of HDCT compared with Adaboost on multiple distributed image recognition.

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