Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
On Combining One-Class Classifiers for Image Database Retrieval
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support Vector Data Description
Machine Learning
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Fisher Discriminants for Outlier Detection
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Kernel PCA for novelty detection
Pattern Recognition
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Learning from Positive and Unlabeled Examples: A Survey
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
Minimum spanning tree based one-class classifier
Neurocomputing
ACM Computing Surveys (CSUR)
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares one-class support vector machine
Pattern Recognition Letters
Information theoretic novelty detection
Pattern Recognition
Gaussian Processes for Object Categorization
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-class classification with gaussian processes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Pruned random subspace method for one-class classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Approximate convex hulls family for one-class classification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Combining structure and appearance for anomaly detection in wire ropes
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Pseudo-density estimation for clustering with gaussian processes
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Novelty detection in wildlife scenes through semantic context modelling
Pattern Recognition
Large-scale gaussian process classification with flexible adaptive histogram kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Rapid uncertainty computation with gaussian processes and histogram intersection kernels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
A framework for evaluating approximation methods for Gaussian process regression
The Journal of Machine Learning Research
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Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This article investigates the use of Gaussian process (GP) priors for this area of research. Focusing on the task of one-class classification, we analyze different measures derived from GP regression and approximate GP classification. We also study important theoretical connections to other approaches and discuss their underlying assumptions. Experiments are performed using a large number of datasets and different image kernel functions. Our findings show that our approaches can outperform the well-known support vector data description approach indicating the high potential of Gaussian processes for one-class classification. Furthermore, we show the suitability of our methods in the area of attribute prediction, defect localization, bacteria recognition, and background subtraction. These applications and experiments highlight the easy applicability of our method as well as its state-of-the-art performance compared to established methods.