A new k-NN based Open-Set Recognition method

Traditional pattern classification methods can handle the objects whose categories are contained in the given (known) categories of training data. In open-set scenarios, objects to classify may belong to the ignorant (unknown) classes that is not included in training data set. Open-Set Recognition (OSR) tries to detect these unknown class objects and classify known class objects. In this paper, we propose a simple OSR method based on k-Nearest Neighbors (k-NNs). The test data (objects to classify) is put together with the labeled training data, so that the labeled training instances and the unlabeled objects to classify can appear in the k-NNs of other objects. Then, the probability of object lying in the given classes can be determined according to the k-NNs of this object. If the labeled training data is the majority of k-NNs, this object most likely belongs to one of the given classes, and the distances between the object and its neighbors are taken into account here. Then the objects with high probability are marked with the estimated probability. However, if most of k-NNs are the unlabeled test objects, the class of object cannot be classified in this step because the k-NNs of the object is uncertain. In this paper, the probability of the other uncertain objects belonging to known classes is re-calculated based on the labeled training instances and the objects marked with the estimated probability. Such iteration will not stop until all the probabilities of objects belonging to known classes are not changed. Then, the Otsu's method is employed to obtain the optimal threshold for the final recognition. If the probability of object belonging to known classes is smaller than this threshold, it will be assigned to the unknown class. The other objects will be considered as known classes and then committed to a specific class by a pre-trained classifier. The effectiveness of the proposed method has been validated using some experiments.