摘要： 针对图像分类中多分类器之间存在互补和冗余信息的特点，提出一种基于Vague融合的图像分类模型。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分类器的分类结果进行决策融合，使多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，分类结果的准确率得到了提高。

关键词: 信息融合, Vague集, 决策融合, 隶属函数

Abstract: According to complementation and redundancy of the multi-classifiers in image classification, this paper proposes a novel approach to image classification based on Vague set. Vague set for positive and negative evidences is applied to analyze and optimize the decisions obtained by multi-classifiers. Through integrating two sides of multiple classification decisions, the classification is optimized and synthesized, thus the processing and the results are both powerful and stable. Experimental results show that the performance of the classification is improved.

Key words: information fusion, Vague set, decision fusion, membership function

中图分类号: 

• TP391