基于拟合型弱分类器的AdaBoost算法

宋鹏峰; 叶庆卫; 陆志华; 周宇

doi:10.11959/j.issn.1000-0801.2019219

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基于拟合型弱分类器的AdaBoost算法

研究与开发 | 更新时间：2024-06-05

- 基于拟合型弱分类器的AdaBoost算法
- AdaBoost algorithm based on fitted weak classifier
- 电信科学 2019年35卷第11期页码：27-35
- 作者机构：
- 作者简介：
  
  [ "宋鹏峰（1995- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为计算机视觉与图像处理" ]
  [ "叶庆卫（1970- ），男，博士，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为信号检测、最优化搜索、视频识别与跟踪等" ]
  [ "陆志华（1983- ），男，博士，宁波大学信息科学与工程学院讲师，主要研究方向信号处理、多运动目标的实时跟踪、统计信号处理算法研究和应用等" ]
  [ "周宇（1960- ），男，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为信号处理、网络与信息安全、物联网技术等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(51675286);国家自然科学基金资助项目;The National Natural Science Foundation of China(61071198)
- DOI：10.11959/j.issn.1000-0801.2019219
  中图分类号： TP391.4
- 网络出版日期：2019-11，
  
  纸质出版日期：2019-11-20
- 稿件说明：
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宋鹏峰, 叶庆卫, 陆志华, 等. 基于拟合型弱分类器的AdaBoost算法[J]. 电信科学, 2019,35(11):27-35.

Pengfeng SONG, Qingwei YE, Zhihua LU, et al. AdaBoost algorithm based on fitted weak classifier[J]. Telecommunications science, 2019, 35(11): 27-35.
宋鹏峰, 叶庆卫, 陆志华, 等. 基于拟合型弱分类器的AdaBoost算法[J]. 电信科学, 2019,35(11):27-35. DOI： 10.11959/j.issn.1000-0801.2019219.

Pengfeng SONG, Qingwei YE, Zhihua LU, et al. AdaBoost algorithm based on fitted weak classifier[J]. Telecommunications science, 2019, 35(11): 27-35. DOI： 10.11959/j.issn.1000-0801.2019219.

摘要

针对AdaBoost算法通过最小化训练错误率来选择弱分类器造成的精度不佳问题以及单阈值作为弱分类器训练过程较慢难以收敛问题，提出了一种基于拟合型弱分类器的AdaBoost算法。首先针对每个特征，在特征值与标记值之间建立映射关系，引入最小二乘法求解拟合多项式函数，并转换成离散分类值，从而获得弱分类器。其次从获得的众多弱分类器中，选择分类误差最小的弱分类器作为本轮迭代的最佳弱分类器，构成新的 AdaBoost 强分类器。与传统训练算法相比，极大地减少了待选弱分类器的个数。选取 UCI 数据集和MIT人脸图像数据库进行实验验证，相较于传统Discrete-AdaBoost算法，改进算法的训练速度提升了一个数量级，人脸检测率可达96.59%。

Abstract

AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate

and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly

the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function

and the continuous fitting values were converted into discrete categorical values

thereby obtaining a weak classifier.From the many classifiers obtained

the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm

the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%.

关键词

Keywords

references

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