We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to recognize if a test instance belongs to the normal class or the abnormal class. Our approach uses the deep features from CNN to feed a pair of MSTs built starting from each test instance. To cut down the computational time we use a parameter $\gamma$ to specify the size of the MST’s starting to the neighbours from the test instance. To prove the effectiveness of the proposed approach we conducted experiments on two publicly available datasets, well-known in literature and we achieved the state-of-the-art results on the CIFAR10 dataset.

我们提出了一种新模型，称为一类最小生成树 (OCmst)，用于新奇检测问题，该模型使用卷积神经网络 (CNN) 作为深度特征提取器和基于最小生成树 (MST) 的基于图的模型。在新奇检测场景中，训练数据不受异常值（异常类）的污染，目标是识别测试实例是属于正常类还是异常类。我们的方法使用来自 CNN 的深度特征来提供从每个测试实例开始构建的一对 MST。为了减少计算时间，我们使用一个参数$\gamma$指定从测试实例开始到邻居的 MST 的大小。为了证明所提出方法的有效性，我们在两个公开可用的数据集上进行了实验，这些数据在文献中是众所周知的，并且我们在 CIFAR10 数据集上取得了最先进的结果。