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ACCV
2010
Springer

One-Class Classification with Gaussian Processes

10 years 8 months ago
One-Class Classification with Gaussian Processes
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.
Michael Kemmler, Erik Rodner, Joachim Denzler
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where ACCV
Authors Michael Kemmler, Erik Rodner, Joachim Denzler
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