PAPER CURRENCY RECOGNITION USING GAUSSIAN MIXTURE MODELS BASED ON STRUCTURAL RISK MINIMIZATION

摘要：

Gaussian Mixture Model (GMM) is a popular tool for density estimation. The parameters of the GMM are estimated based on Maximum Likelihood principle (MLP) in almost all recognition system. However, the number of mixtures used in the model is important for determining the models effectiveness; the general problem of mixture modeling is difficult when the number of components is unknown. This paper presents paper currency recognition using GMM based on Structural Risk Minimization (SRM). By selecting the proper number of the components with SRM, the system can overcome the demerit by the number of the Gaussian components selected artificially. A total number of 8 bill types including 5, 10(new and old model),20, 50(new and old model),100(new and old model) are considered as classification categories. The experiments show that GMM which employs SRM is a more flexible alternative and lead to improved results for Chinese paper currency recognition.

关键词： Image recognition Gaussian mixture model Structural Risk Minimization

作者: FAN-HUI KONG JI-QUAN MA JIA-FENG LIU

作者单位: Institute of Information Science and Technology, Heilongjiang University, Harbin 150080, China Institute of Computer Science and Technology, Heirongjiang University, Harbin 150080, China;Institut Institute of Computer Science, Harbin Institute of Technology, Harbin 150001, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3213-3217

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）