计算机应用 ›› 2014, Vol. 34 ›› Issue (1): 260-264.DOI: 10.11772/j.issn.1001-9081.2014.01.0260

• 人工智能 • 上一篇    下一篇

FCM预选取样本的半监督SVM图像分类方法

陈永健,汪西莉   

  1. 陕西师范大学 计算机科学学院,西安 710062
  • 收稿日期:2013-07-23 修回日期:2013-09-11 出版日期:2014-01-01 发布日期:2014-02-14
  • 通讯作者: 汪西莉
  • 作者简介:陈永健(1988-),男,福建三明人,硕士研究生,主要研究方向:模式识别、图像处理;汪西莉(1969-),女,陕西西安人,教授,博士生导师,博士,主要研究方向:智能信息处理、模式识别、图像处理。
  • 基金资助:

    国家自然科学基金资助项目

Semi-supervised SVM image classification method with pre-selected sample by fuzzy C-mean

CHEN Yongjian,WANG Xili   

  1. School of Computer Science, Shaanxi Normal University, Xi'an Shaanxi 710062, China
  • Received:2013-07-23 Revised:2013-09-11 Online:2014-01-01 Published:2014-02-14
  • Contact: WANG Xili

摘要: 针对基于拉普拉斯支持向量机(LapSVM)的半监督分类方法需要将全部无标记样本加入训练样本集中训练得到分类器,算法需要的时间和空间复杂度高,不能有效处理大规模图像分类的问题,提出了模糊C-均值聚类(FCM)预选取样本的LapSVM图像分类方法。该方法利用FCM算法对无标记样本聚类,根据聚类结果选择可能在最优分类超平面附近的无标记样本点加入训练样本集,这些样本可能是支持向量,携带对分类有用的信息,其数量只是无标记样本的一少部分,因此使训练样本集减小。计算机仿真结果表明该方法充分利用了无标记样本所蕴含的判别信息,有效地提高了分类器的分类精度,降低了算法的时间和空间复杂度。

关键词: 支持向量机, 半监督学习, 预选取样本, 模糊C-均值聚类, 图像分类

Abstract: In order to solve the problems that the semi-supervised classification method based on Laplacian Support Vector Machines (LapSVM) requires that all unlabeled sample should be added to the training set to train a classifier, the algorithm demands high time and space and cannot effectively deal with large-scale image classification, Fuzzy C-Mean (FCM) pre-selected sample of LapSVM image classification method was proposed. The method used FCM algorithm for clustering the unlabeled samples. According to the clustering results, unlabeled samples of near optimal separating hyper-plane were selected to add to the training sample set, and these samples may be support vector carrying useful information for classification. The quantity was only a small part of the unlabeled samples, so the training sample set was reduced. The simulation results show that this method takes advantage of the inherent discrimination information of the unlabeled samples, effectively improves the accuracy of classifiers, and reduces the algorithm's time and space complexity.

Key words: Support Vector Machine (SVM), semi-supervised learning, pre-selected sample, Fuzzy C-means Clustering (FCM), image classification

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