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Kernel Null Space Methods for Novelty Detection

Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, and Joachim Denzler



Paul Bodesheim and Alexander Freytag and Erik Rodner and Michael Kemmler and Joachim Denzler. Kernel Null Space Methods for Novelty Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2013. (accepted for publication). [pdf] [bib]

Code available!

The Matlab source code is available [here].



Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to model the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and ImageNet. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other approaches.