结合图模型的优化多类SVM及智能交通应用
发布时间：2018-11-25 12:45
【摘要】：为提高多类支持向量机分类器对多目标的分类准确度,提出一种结合无向图模型优化的多类支持向量机分类器。首先,利用余弦测度计算训练数据之间的相似度,构建包含训练数据和相似度矩阵的无向图模型,求解相似度约束矩阵。然后,将相似度约束矩阵引入多类支持向量机求解的目标函数,构建优化的多类支持向量机分类器。最后,将优化的多类支持向量机分类器用于智能交通领域,结合梯度方向直方图特征检测行人和车辆目标。实验表明,该方法检测行人和车辆目标的错误率低于经典的多类支持向量机分类器和目前主流的目标检测方法。
[Abstract]:In order to improve the classification accuracy of multi-class support vector machine classifier, a multi-class support vector machine classifier combined with undirected graph model optimization is proposed. Firstly, using cosine measure to calculate the similarity between training data, an undirected graph model including training data and similarity matrix is constructed, and the similarity constraint matrix is solved. Then, the similarity constraint matrix is introduced into the objective function of multi-class support vector machine, and the optimized multi-class support vector machine classifier is constructed. Finally, the optimized multi-class support vector machine classifier is used to detect pedestrian and vehicle targets in the intelligent transportation field, combining with gradient direction histogram features. Experiments show that the error rate of this method for detecting pedestrian and vehicle targets is lower than that of classical multi-class support vector machine classifiers and the current mainstream target detection methods.
【作者单位】： 常熟理工学院计算机科学与工程学院;郑州成功财经学院信息工程系;湖北大学计算机与信息工程学院;
【基金】：江苏省高校自然科学研究项目(12KJB520001)
【分类号】：U495;TP391.41
，
本文编号：2356155
[Abstract]:In order to improve the classification accuracy of multi-class support vector machine classifier, a multi-class support vector machine classifier combined with undirected graph model optimization is proposed. Firstly, using cosine measure to calculate the similarity between training data, an undirected graph model including training data and similarity matrix is constructed, and the similarity constraint matrix is solved. Then, the similarity constraint matrix is introduced into the objective function of multi-class support vector machine, and the optimized multi-class support vector machine classifier is constructed. Finally, the optimized multi-class support vector machine classifier is used to detect pedestrian and vehicle targets in the intelligent transportation field, combining with gradient direction histogram features. Experiments show that the error rate of this method for detecting pedestrian and vehicle targets is lower than that of classical multi-class support vector machine classifiers and the current mainstream target detection methods.
【作者单位】： 常熟理工学院计算机科学与工程学院;郑州成功财经学院信息工程系;湖北大学计算机与信息工程学院;
【基金】：江苏省高校自然科学研究项目(12KJB520001)
【分类号】：U495;TP391.41
，
本文编号：2356155
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