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An evaluation of open set recognition for FLIR images

机译：FLIR图像开放集识别的评估

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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标签输入根据培训阶段中学到的内容。因此，培训中未看出的测试输入总是给出不正确的标签。打开Set识别算法通过考虑训练中不存在的输入来解决此问题，并为分类器提供“拒绝”未知样本的分类器。在文献中已经开发了许多这些技术，其中许多是基于支持向量机（SVM）。使用SVM超平板，一种方法，1-VS-Set机器，在特征空间中构建“平板”。落在板坯的一侧或板内的输入属于培训类，而落在板坯的远侧的输入被拒绝。我们注意到，通过阈值平等的后验概率可以实现未知输入的拒绝。另一个最近开发的方法，概率开放式SVM（POS-SVM），虚拟地确定良好的概率阈值。我们将1-VS-Set Machine，POS-SVM和关闭的SEL SVMS应用于来自Comanche SIG数据集的FLIR图像。数据集中的车辆分为三个一般课程：轮式，装甲人员承运人（APC）和坦克。对于每个阶级，拍摄粗糙的姿态估计（前，后，左，右）。在封闭式的术中，我们分析了这些算法以预测车辆类和姿势。要测试开放式性能，请从培训中保持一个或多个车辆类。通过分别考虑关闭和开放的套装性能，我们可能会密切分析阶级间歧视和阈值效率。

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