摘要
在分块核函数的基础上提出了基于多个图像特征进行组合决策的识别方法。该算法先对交通标识图像提取两个不同的特征,即HOG特征和基于子模式组合的分块核函数特征,然后针对不同特征构造相应的分类器,最后对这几个分类器的输出采用投票机制进行决策融合。在德国交通标识数据库上的实验结果表明,该方法相比单特征识别具有更高的识别准确率。
We present a recognition method based on multiple graphic features to do decision combination on the basis of block kernel function. Firstly, the algorithm extracts two different characteristics of traffic signs, i.e. HOG features and the feature of block kernel function based on sub-mode combination. Secondly,we construct different classifiers for different features accordingly. Finally, the decision fusion is done by the voting mechanism for the outputs of both classifiers. Experiments on Germany Traffic Signs Database prove that our proposal has a higher recognition accuracy compared with the single-feature recognition.
出处
《计算机工程与科学》
CSCD
北大核心
2015年第4期776-782,共7页
Computer Engineering & Science
基金
国家自然科学基金资助项目(90820306)
高维信息智能感知与系统教育部重点实验室(南京理工大学)开放基金资助项目(MEKL201308)
关键词
核Fisher非线性鉴别分析
特征组合
分块核方法
交通标识识别
kernel Fisher nonlinear discriminate analysis
feature combination
block kernel function
traffic sign recognition
