典型支持向量机包围盒的联合标定
Joint calibration of Ensemble of Exemplar SVMs
摘要：
本文提出了一种对样本支持向量机模型的包围圈进行标定的方法。与独立校准每个SVM的标准方法不同，我们的方法优化了它们作为集成的联合性能。我们制定联合校准作为一个约束优化问题，并设计了一个有效的优化算法，以找到其全局最优。该算法动态地丢弃早期不能包含最优解的部分解空间，使优化在计算上可行。我们使用在最先进的CNN描述符上训练的EE-SVM进行实验。ILSVRC 2014和PASCAL VOC 2007数据集的结果表明：（i）我们的联合校准程序在将窗口分类为是否属于对象类的任务上优于独立校准;（ii）这种改进的窗口分类器可以带来更好的性能在目标检测任务上。
英文摘要：
We present a method for calibrating the Ensemble of Exemplar SVMs model. Unlike the standard approach, which calibrates each SVM independently, our method optimizes their joint performance as an ensemble. We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum. The algorithm dynamically discards parts of the solution space that cannot contain the optimum early on, making the optimization computationally feasible. We experiment with EE-SVM trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and PASCAL VOC 2007 datasets show that (i) our joint calibration procedure outperforms independent calibration on the task of classifying windows as belonging to an object class or not; and (ii) this improved window classifier leads to better performance on the object detection task.
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