One-Class Classification with Gaussian Processes

Abstract

Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This paper investigates the use of Gaussian process (GP) priors for this area of research. Focusing on the task of one-class classification for visual object recognition, we analyze different measures derived from GP regression and approximate GP classification. Experiments are performed using a large set of categories and different image kernel functions. Our findings show that the well-known Support Vector Data Description is significantly outperformed by at least two GP measures which indicates high potential of Gaussian processes for one-class classification.