利用支持向量机识别汽车颜色

Vehicle Color Recognition Using Support Vector Machine

大类别数分类时支持向量机 (SVM)数量较多 ,文中通过类别合并和特征空间分解 ,结合决策树判别方法 ,对SVM数量进行优化 ,提出了一种基于优化SVM的汽车颜色识别方法 该方法与最近邻分类方法相比 ,无论是在速度上还是识别正确率上都得到了提高 实验结果表明 ,该方法是一种快速且正确率较高的多类别分类方法 。

Traditionally, the number of SVM required for classification increases exponentially with the category number In our approach, some SVMs are omitted by merging several categories into one and dividing a feature space into several subspaces Combining with the decision tree classification method, a SVM based vehicle color recognition approach is proposed by optimizing the number of SVM In comparison with the nearest neighbor classification method, the proposed approach is superior in recognition speed and accuracy Experimental results show the proposed solution is an effective multi category classifier and can be applied to real time recognition cases