One-class label propagation using local cone based similarity
Takumi Kobayashi, Nobuyuki Otsu · 2011
In this paper, we propose a novel method of label propagation for one-class learning. For binary (positive/negative) classification, the proposed method simultaneously measures the pair-wise similarity between samples and the negativity at every sample based on a cone-based model of local neighborhoods. Relying only on positive labeled samples as in one-class learning, the method estimates the labels of unlabeled samples via label propagation using the similarities and the negativities in the framework of semi-supervised learning. In the proposed method, unlike standard label propagation methods, it is not necessary to prepare negative labeled samples since the measured negativity works as an alternative of such labeling negative samples. In experiments on target detection in still images and motion images, the proposed method exhibits the favorable performances compared to the other methods.