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 modeling 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 methods.

Twenty-seven marine sediment-and sponge-derived actinomycetes with a preference for or dependence on seawater for growth were classified at the genus level using molecular taxonomy. Their potential to produce bioactive secondary metabolites was analyzed by PCR screening for genes involved in polyketide and nonribosomal peptide antibiotic synthesis. Using microwell cultures, conditions for the production of antibacterial and antifungal compounds were identified for 15 of the 27 isolates subjected to this screening. Nine of the 15 active extracts were also active against multiresistant Gram-positive bacterial and/or fungal indicator organisms, including vancomycin-resistant Enterococcus faecium and multidrug-resistant Candida albicans. Activityguided fractionation of fermentation extracts of isolate TFS65-07, showing strong antibacterial activity and classified as a Nocardiopsis species, allowed the identification and purification of the active compound. Structure elucidation revealed this compound to be a new thiopeptide antibiotic with a rare aminoacetone moiety. The in vitro antibacterial activity of this thiopeptide, designated TP-1161, against a panel of bacterial strains was determined.

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

No abstract

No abstract