Fusing Global and Regional Features for Image Classification
This paper presents a novel approach to image classification based on the fusion of global and regional features, which are helpful to describe image semantics to classification, in which vague sets 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 results will be both powerful and stable. Experimental results show that the performance of the classification is greatly improved.
vague set decision fusion support vector machine
Xiaohong Hu Xu Qian
School of Information and Management Science, Henan Agricultural University, Zhengzhou, 450002, Chin School of Mechanical Electronic and Information Engineering, China University of Mining and Technolo
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5970-5973
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）