摘要
针对图像分类中传统的特征融合方式所形成的巨大特征空间甚至维数灾难问题,提出了一种基于vague融合的图像分类模型。通过同时给出支持和反对的证据,运用vague集的真假隶属函数对图像分类中多特征分类器的分类结果进行决策融合,多特征分类器的分类结果得到优化和综合,从而获得更准确、更稳定的决策分类结果。实验结果表明,运用此决策融合模型是可行的,同时,图像分类准确率得到了明显提高。
For traditional way of image classification,feature fusion scheme would decrease classificatory quality or result in other problems such as curse of dimensionality.This paper proposed a novel approach trying to integrate different features in image classification.Vague set for positive and negative evidences was applied to analyze and optimize the decisions obtained by multi-classifiers.Through integrating two sides of multiple classification decisions,the classification was optimized and synthesized,thus t...
出处
《计算机应用研究》
CSCD
北大核心
2009年第2期787-788,794,共3页
Application Research of Computers
基金
国家教育部科研重点资助项目(107021)
关键词
信息融合
模糊集
维数灾难
隶属函数
information fusion
vague set
curse of dimensionality
membership function
