Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (5): 169-172.DOI: 10.3778/j.issn.1002-8331.2010.05.052

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

Image classification based on feature selection and SVMs

GAO Yong-gang1,ZHOU Ming-quan2,GENG Guo-hua1,LIU Yan-wu1   

  1. 1.Department of Information Science & Technology,Northwest University,Xi’an 710127,China
    2.Department of Information Science & Technology,Beijing Normal University,Beijing 100875,China
  • Received:2008-08-25 Revised:2008-11-17 Online:2010-02-11 Published:2010-02-11
  • Contact: GAO Yong-gang



  1. 1.西北大学 信息科学与技术学院,西安 710127
    2.北京师范大学 信息科学与技术学院,北京 100875
  • 通讯作者: 高永岗

Abstract: This paper focuses on the MI-based image feature selection method[1],briefly describes SVMs category principle and design[2].The paper proposes the MI greedy optimal algorithm,transforms high-dimensional data processing into a one-dimensional data processing,simplifies the difficulty of the operation and increases the speed and accuracy of classification.The results show that,the eight classification,more than 1,000 classified images are better dealt with by the MI greedy optimal algorithm than the traditional classification algorithm.

Key words: feature selection, MI greedy optimal algorithm, Support Vector Machines(SVMs)

摘要: 重点论述了基于MI图像特征选择方法[1],简要地讲述了支持向量机的SVMs分类器原理和设计[2]。提出了MI贪婪最优算法,将高维数据处理转化为一维数据处理,简化了运算难度,同时提高了分类速度和准确性。实验结果表明,通过对8个分类、上千张图片进行分类处理,效果好于传统的分类算法。

关键词: 特征选择, MI贪婪最优算法, 支持向量机(SVMs)

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