Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (33): 156-158.DOI: 10.3778/j.issn.1002-8331.2009.33.051

• 图形、图像、模式识别 • Previous Articles     Next Articles

Cascade detectors with linear SVM based on geometry

AN Ping1,WU Tao2,HE Han-gen2   

  1. 1.Astronaut Centre of China, Beijing 100193,China
    2.School of Machatronics and Automation,National University of Defense Technology,Changsha 410073,China
  • Received:2008-06-30 Revised:2008-10-20 Online:2009-11-21 Published:2009-11-21
  • Contact: AN Ping

基于L-SVM的级联检测器的构造

安 平1,吴 涛2,贺汉根2   

  1. 1.中国航天员科研训练中心,北京 100193
    2.国防科学技术大学 机电工程与自动化学院自动化研究所,长沙 410073
  • 通讯作者: 安 平

Abstract: To detect objects quickly,a new method is presented to construct a cascade of linear classifiers with L-SVM(Lagrangian Support Vector Machine,L-SVM).At first,the negative data is divided into several parts accumulatively according to the geometric distribution of the training data.Here,every part of negative data is separable with the positive data;Second,L-SVM is used to obtain the linear classifiers between every part of negative data and positive data;At last,the linear classifiers are combined to construct a cascade detector.The experiments show that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.

Key words: cascade, geometry, Lagrangian Support Vector Machine(LSVM)

摘要: 为了实现目标的快速检测,提出了一种新的基于拉格朗日支持向量机(L-SVM)的线性级联式分类器的构造方法。该方法首先根据样本的几何分布,用迭代的方式把负样本分成若干部分与正样本线性可分的样本;然后用L-SVM对这些正负样本进行分类,得到若干个线性分类器;最后,将这些线性分类器顺次组合,构成级联分类器。实验表明,与经典非线性SVM分类器相比,这种分类器在保持SVM较强泛化性能的优点的同时,在检测效率方面更是具有明显的优势。

关键词: 级联, 几何, 拉格朗日支持向量机

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