计算机应用 ›› 2010, Vol. 30 ›› Issue (4): 990-992.

• 人工智能 • 上一篇    下一篇

基于对象模糊密度赋值的决策层融合算法

陈亚必1,朱勇2,詹永照2   

  1. 1. 江苏大学计算机科学与通信工程学院
    2.
  • 收稿日期:2009-09-08 修回日期:2009-10-30 发布日期:2010-04-15 出版日期:2010-04-01
  • 通讯作者: 陈亚必
  • 基金资助:
    基于动态图形模型和音视频的情感识别方法的研究

Decision level fusion algorithm with fuzzy densities determined by object

  • Received:2009-09-08 Revised:2009-10-30 Online:2010-04-15 Published:2010-04-01
  • Contact: Ya-Bi CHEN

摘要: 针对模糊积分在进行决策层融合识别时,其模糊密度是根据已知类别样本的先验静态信息赋值的,并不能根据具体对象的识别结果进行动态调整使之更接近现实的情形,提出了一种基于对象模糊密度赋值的决策层融合算法。该算法利用各分类器识别具体对象时给出的客观信息计算出其所属类别的区分度,再结合先验静态信息对模糊密度进行动态赋值。将该算法应用于人脸表情识别,实验结果表明,获得了较好的融合效果,提高了表情识别的准确率。

关键词: 决策层融合算法, 模糊积分, 区分度, 对象模糊密度, 混淆矩阵, 表情识别

Abstract: The fuzzy densities of fuzzy integral are determined by the priori static information of labeled samples and cannot be dynamically adjusted by the recognition result of specific objects to be realistic. To overcome this disadvantage, a decision level fusion algorithm with fuzzy integral whose fuzzy densities were determined dynamically by objects was presented. In this algorithm, the fuzzy densities were determined dynamically by combining the discriminabiltity computed from the objective information given by the classifiers after recognizing the specific object and the priori static information. This algorithm was applied to facial expression recognition. The experimental results show that the proposed algorithm can obtain a better fusion result and raise the accuracy of expression recognition.

Key words: decision level fusion algorithm, fuzzy integral, discriminabiltity, fuzzy object density, confusion matrix, expression recognition