SVM应用的例子.PPT

* * * * * * * * * * * * * * * * 特征空间的隐式映射：核函数 ?? 用之前的方法将限制或约束条件加入到目标函数中，得到新的拉格朗日函数，进行前文中的运算。?? ?? ?? ??? * SVM应用的例子 Object Tracking With Multi-View Support Vector Machines Abstract—How to build an accurate and reliable appearancemodel to improve the performance is a crucial problem inobject tracking. Since the multi-view learning can lead to moreaccurate and robust representation of the object, in this paper,we propose a novel tracking method via multi-view learningframework by using multiple support vector machines (SVM).The multi-view SVMs tracking method is constructed based onmultiple views of features and a novel combination strategy. Torealize a comprehensive representation, we select three differenttypes of features, i.e., gray scale value, histogram of oriented gradients (HOG), and local binary pattern (LBP), to train the corresponding SVMs. These features represent the object from the perspectives of description, detection, and recognition,respectively. In order to realize the combination of the SVMs under the multi-view learning framework, we present a novel * 特征空间的隐式映射：核函数 collaborative strategy with entropy criterion, which is acquired by the confidence distribution of the candidate samples. In addition, to learn the changes of the object and the scenario, we propose a novel update scheme based on subspace evolution strategy. The new scheme can control the model update adaptively and help to address the occlusion problems. We conduct our approach on several public video sequences and the experimental results demonstrate that our method is robust and accurate, and can achieve the state-of-the-art tracking performance. Index Terms—Entropy criterion, multi-view learning, object tracking, subspace evolution, support vector machines (SVM). ?? ?? ??? * 特征空间的隐式映射：核函数 * 特征空间的隐式映射：核函数 * #include opencv2/opencv.hpp extern C { #include vl/generic.h #include vl/lbp.h #include vl/hog.h float SVMweights[3];//Grey Hog Lbp CvSVM SVM1; CvSVM SVM2;