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AdaBoost及其改进算法综述 被引量：27

Review of AdaBoost and Its Improvement
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摘要 AdaBoost算法是目前人脸检测领域最有效的方法之一,自该算法提出以来,很多研究者做了深入的研究分析和改进工作。基于AdaBoost算法受到众多研究者的重视,综述了AdaBoost及其改进算法。从AdaBoost算法出发,着重分析了AdaBoost算法的优缺点,并以此为基础对其改进算法作系统的分析和介绍,对改进算法进行了简单归类。最后,指出了算法未来的几个发展方向。 The AdaBoost algorithm is one of the most effective methods in area of Face Detection.Since this algorithm has been proposed,many indepth research and analysis and improvement was provided by researchers.This paper reviewed AdaBoost algorithm and its improvement.Beginning with AdaBoost algorithm,we focus on analyzing the adavantages and disadvantages of AdaBoost algorithm.After this,we made systematic analysis and presentation on these improvements,including simple classification.Finally,we conclude with several promising directions for future research.
作者 廖红文 周德龙
机构地区 浙江工业大学计算机科学与技术学院
出处 《计算机系统应用》 2012年第5期240-244,共5页 Computer Systems & Applications
关键词 ADABOOST 分类器 特征 人脸检测 积分图像 AdaBoost classifier feature face detection integral image
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献41

  • 1Freund Y, Schipare RE. A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory: Second European Conference, 1995: 23-37.
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  • 3Viola P, Jones MJ. Robust real-time face detection. International Journal of Computer Vision, 2004,57(2):137- 154.
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二级参考文献33

  • 1马勇,丁晓青.Real-Time Multi-View Face Detection and Pose Estimation Based on Cost-Sensitive AdaBoost[J].Tsinghua Science and Technology,2005,10(2):152-157. 被引量：4
  • 2王晓丹,孙东延,郑春颖,张宏达,赵学军.一种基于AdaBoost的SVM分类器[J].空军工程大学学报（自然科学版）,2006,7(6):54-57. 被引量：23
  • 3Robert E S. Theoretical views of boosting[C]// Proceedings of European Conference on Computational Learning Theory. Nordkirchen, Germany : Springer-Verlag, 1999 : 1 - 10.
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  • 6Paul A V, Michael J J. Rapid object detection using a boosted cascade of simple features[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA: IEEE Computer Society, 2001: 511-518.
  • 7HUANG Chang, AI Haizhou, LI Yuan, et al. High-performance rotation invariant multiview face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4): 671- 686.
  • 8Llew M, Jonathan B, Peter L B, et al. Boosting algorithms as gradient descent [C]// Proceedings of Advances in Neural Information Processing Systems. USA: MIT Press, 1999: 512-518.
  • 9LI Ling. Data Complexity in machine learning and novel classification algorithms[D]. USA: California Institute of Technology, 2006.
  • 10Matti A, Jorma L. Using diversity of errors for selecting members of a committee classifier [J]. Pattern Recognition, 2006, 39(4) : 608- 623.

共引文献56

同被引文献232

引证文献27

二级引证文献159

计算机系统应用
2012年 第5期

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