  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 to 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 CIFAR10 dataset.
