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An evaluation of open set recognition for FLIR images

机译：FLIR图像的开放集识别评估

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Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to "reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a "slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.
机译：典型的监督分类算法根据在培训阶段学到的内容来标记输入。因此，在训练中没有看到的测试输入总是被给以不正确的标签。开放集识别算法通过考虑训练中不存在的输入并为分类器提供“拒绝”未知样本的选项来解决此问题。文献中已经开发了许多这样的技术，其中许多是基于支持向量机（SVM）的。一种方法是1-vs-set机器，它使用SVM超平面在特征空间中构造“平板”。落在板的一侧或板内的输入属于训练班，而落在板的另一侧的输入则被拒绝。我们注意到拒绝未知输入可以通过对类后验概率进行阈值化来实现。最近开发的另一种方法是概率开放集SVM（POS-SVM），根据经验确定好的概率阈值。我们将1-vs-set机器，POS-SVM和封闭集SVM应用于从Comanche SIG数据集获取的FLIR图像。数据集中的车辆分为三大类：轮式，装甲运兵车（APC）和坦克。对于每个课程，都会进行粗略的姿势估计（前，后，左，右）。在封闭的意义上，我们分析了这些算法，以预测车辆的类别和姿态。为了测试开放式表演的性能，必须对一种或多种车辆类别进行培训。通过分别考虑封闭式和开放式绩效，我们可以密切分析阶级间的歧视和门槛有效性。

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