Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (13): 11-14.DOI: 10.3778/j.issn.1002-8331.2010.13.004

• 博士论坛 • Previous Articles     Next Articles

Multi-class active learning approach for image classification

LIU Jun1,2,XIONG Zhong-yang1,WANG Yin-hui1   

  1. 1.College of Computer Science,Chongqing University,Chongqing 400030,China
    2.College of Science and Technology,Chongqing Radio & TV University,Chongqing 400052,China
  • Received:2010-01-25 Revised:2010-03-12 Online:2010-05-01 Published:2010-05-01
  • Contact: LIU Jun


刘 君1,2,熊忠阳1,王银辉1   

  1. 1.重庆大学 计算机学院,重庆 400030
    2.重庆广播电视大学 理工学院,重庆 400052
  • 通讯作者: 刘 君

Abstract: A multi-class active learning approach for image classification is described by using Decision Directed Acyclic Graph Support Vector Machine,featured by rapid decision-making,as basic classifier and the ideas of active learning are combined.This approach is a semi-automatic classification method for image semantics,and can be used to divide images into different categories.In this approach,an active selection strategy based on the level of doubts is presented on the basis of the widely-used method of selecting the closest samples to the dividing hyperplane.This strategy extends the strategy of selecting the closest ones to the SVM dividing hyperplane specified in SVMactive algorithm to multi-class classification,and at the same time,treats odd samples and easily mistaken samples differently.In this way,the interference of noise on classifier is reduced,and the accuracy of classification is increased.

Key words: support vector machine, multi-class classification problem, decision directed acyclic graph, active learning

摘要: 以决策速度快的决策导向非循环图支持向量机(Decision Directed Acyclic Graph Support Vector Machine)为基准分类器,结合主动学习的思想,提出了一种图像多分类主动学习方法。这种方法是一种半自动的图像语义分类方法,可以将图像分成多个语义类别。该方法在最近边界主动选择方法的基础上,提出一种基于质疑度的主动选择策略。这种策略将SVMactive中提出的最近邻SVM分类面选择的反馈样例策略延伸到多分类中,通过区别对待奇异样例和容易错分样例,减少了噪声数据对分类器的干扰,提高了分类的精度。

关键词: 支持向量机, 多分类, 决策导向非循环图, 主动学习

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