摘要
针对现有车牌识别系统效率低的问题,提出了一种改进的支持向量机算法。首先对车牌进行预处理和定位,将每个特征区域构建一个多核心组合。以半定规划求解最佳的权系数。使用改进的半定规划来解决多核学习算法,降低搜索空间。最后构建车牌识别模型。仿真实验表明,该算法效率高,稳定性好。
An improved support vector machine will be presented for solving the problem of the inefficiency of license plate recognition.License plate pretreated and location is the first step of the automatically license plate recognition,and we define one multiple kernel for each feature.The optimal weight with each kernel matrix in the combination is obtained through the semi-definite programming learning method.We solve multiple kernel learning with an improved semi-definite programming to reduce the time and space requirements.Last we build the model of license plate recognition.Experimental results show that our algorithm is fast and accurate.
出处
《激光与红外》
CAS
CSCD
北大核心
2010年第5期568-572,共5页
Laser & Infrared
关键词
支持向量机
多核学习
半定规划
车牌识别
support vector machine
multiple kernel learning
semi-definite programming
license plate recognition
