计算机应用 ›› 2015, Vol. 35 ›› Issue (9): 2596-2601.DOI: 10.11772/j.issn.1001-9081.2015.09.2596

• 人工智能 • 上一篇    下一篇

负样本信息继承的矩阵式瀑布分类器高效学习算法

刘阳, 闫胜业, 刘青山   

  1. 南京信息工程大学 信息与控制学院, 南京 210044
  • 收稿日期:2015-04-09 修回日期:2015-05-27 出版日期:2015-09-10 发布日期:2015-09-17
  • 通讯作者: 刘阳(1990-),男,江苏徐州人,硕士研究生,主要研究方向:人体目标检测,1121412518@qq.com
  • 作者简介:闫胜业(1978-),男,河南新乡人,教授,博士,主要研究方向:物体检测与识别、物体跟踪、特征点定位;刘青山(1975-),男,安徽庐江人,教授,博士,主要研究方向:图像分析、视频分析、机器学习。

Matrix-structural fast learning of cascaded classifier for negative sample inheritance

LIU Yang, YAN Shengye, LIU Qingshan   

  1. College of Information and Control, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China
  • Received:2015-04-09 Revised:2015-05-27 Online:2015-09-10 Published:2015-09-17

摘要: 针对矩阵式瀑布分类器学习算法在负样本自举过程中无法快速自举出训练所需的高质量样本,自举过程严重影响整体学习效率及最终检测器性能等问题,提出了一种高效学习算法——负样本信息继承的矩阵式瀑布分类器高效学习算法。其自举负样本过程为样本继承与层次自举相结合,首先从训练上一层强分类器所用的负样本集中继承有效负样本,样本集不足部分再从负图像集中自举。样本继承压缩了有效样本的自举范围,可以快速自举出训练所需样本;并且自举负样时对样本进行预筛选,增加了样本复杂度,提升了最终分类器性能。实验结果表明:训练完成方面,本算法比矩阵式瀑布分类器算法节省20h;检测性能方面,比矩阵式瀑布型分类器高出1个百分点;与其他17种人体检测算法性能相比也有很好的性能表现。所提算法较矩阵式瀑布分类器学习算法在训练效率及检测性能上都有很大提升。

关键词: 瀑布型分类器, 自举, 负样本, 训练时间

Abstract: Due to the disadvantages such as inefficiency of getting high-quality samples, bad impact of bootstrap to the whole learning-efficiency and final classifier performance in the negative samples bootstrap process of matrix-structural learning of cascade classifier algorithm. This paper proposed a fast learning algorithm-matrix-structural fast learning of cascaded classifier for negative sample inheritance. The negative sample bootstrap process of this algorithm combined sample inheritance and gradation bootstrap, which inherited helpful samples from the negative sample set used by last training stage firstly, and then got insufficient part of sample set from the negative image set. Sample inheritance reduced the bootstrap range of useful samples, which accelerated bootstrap. And sample pre-screening, during bootstrap process, increased sample complexity and promoted final classifier performance. The experiment results show that the proposed algorithm saves 20h in training time and improves 1 percentage point in detection performance, compared with matrix-structural learning of cascaded classifier algorithm. Besides, compared with other 17 human detection algorithms, the proposed algorithm achieves good performance too. The proposed algorithm gets great improvement in training efficiency and detection performance compared with matrix-structural learning of cascaded classifier algorithm.

Key words: cascade classifier, bootstrap, negative sample, training time

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