2019

Classification-Specific Parts for Improving Fine-Grained Visual Categorization.
Korsch, Dimitri and Bodesheim, Paul and Denzler, Joachim.
Pattern Recognition. Pages 62-75. 2019.
[bibtex] [pdf] [web] [code] [abstract]

2017

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques.
Milan Flach and F. Gans and Alexander Brenning and Joachim Denzler and Markus Reichstein and Erik Rodner and Sebastian Bathiany and Paul Bodesheim and Yanira Guanche and Sebasitan Sippel and Miguel D. Mahecha.
Earth System Dynamics. 8 (3): pages 677-696. 2017.
[bibtex] [web]
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks.
Erik Rodner and Alexander Freytag and Paul Bodesheim and Björn Fröhlich and Joachim Denzler.
International Journal of Computer Vision (IJCV). 121 (2): pages 253-280. 2017.
[bibtex] [pdf] [web]

2016

Maximally Divergent Intervals for Anomaly Detection.
Erik Rodner and Björn Barz and Yanira Guanche and Milan Flach and Miguel Mahecha and Paul Bodesheim and Markus Reichstein and Joachim Denzler.
ICML Workshop on Anomaly Detection (ICML-WS). 2016. Best Paper Award
[bibtex] [pdf] [code]
Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations.
Milan Flach and Miguel Mahecha and Fabian Gans and Erik Rodner and Paul Bodesheim and Yanira Guanche-Garcia and Alexander Brenning and Joachim Denzler and Markus Reichstein.
European Geosciences Union General Assembly. 2016.
[bibtex] [pdf] [web]
Multivariate Anomaly Detection for Earth Observations: A Comparison of Algorithms and Feature Extraction Techniques.
Milan Flach and Fabian Gans and Alexander Brenning and Joachim Denzler and Markus Reichstein and Erik Rodner and Sebastian Bathiany and Paul Bodesheim and Yanira Garcia Guanche and Sebastian Sippel and Miguel Mahecha.
Earth System Dynamics. 2016. in discussion
[bibtex] [web]

2015

Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances.
Christoph Käding and Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 4343-4352. 2015.
[bibtex] [pdf] [web] [code] [presentation] [supplementary] [abstract]
Local Novelty Detection in Multi-class Recognition Problems.
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 813-820. 2015.
[bibtex] [pdf] [supplementary]

2014

Temporal Video Segmentation by Event Detection: A Novelty Detection Approach.
Mahesh Venkata Krishna and Paul Bodesheim and Marco Körner and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 24 (2): pages 243-255. 2014.
[bibtex] [pdf] [web]
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods.
Alexander Freytag and Johannes Rühle and Paul Bodesheim and Erik Rodner and Joachim Denzler.
International Conference on Pattern Recognition (ICPR) - FEAST workshop. 2014. Best Poster Award
[bibtex] [pdf] [code] [presentation] [abstract]

2013

Kernel Null Space Methods for Novelty Detection.
Paul Bodesheim and Alexander Freytag and Erik Rodner and Michael Kemmler and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 3374-3381. 2013.
[bibtex] [pdf] [web] [code] [presentation]
Video Segmentation by Event Detection: A Novel One-class Classification Approach.
Mahesh Venkata Krishna and Paul Bodesheim and Joachim Denzler.
4th International Workshop on Image Mining. Theory and Applications (IMTA-4). 2013.
[bibtex] [pdf]
An Efficient Approximation for Gaussian Process Regression.
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler. (2013) Technical Report TR-FSU-INF-CV-2013-01
[bibtex] [pdf]
Beyond the closed-world assumption: The importance of novelty detection and open set recognition.
Joachim Denzler and Erik Rodner and Paul Bodesheim and Alexander Freytag.
GCPR Workshop on Unsolved Problems in Pattern Recognition (GCPR-WS). 2013.
[bibtex] [pdf]
Approximations of Gaussian Process Uncertainties for Visual Recognition Problems.
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
Scandinavian Conference on Image Analysis (SCIA). Pages 182-194. 2013.
[bibtex] [pdf]
Labeling examples that matter: Relevance-Based Active Learning with Gaussian Processes.
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 282-291. 2013.
[bibtex] [pdf] [code] [supplementary] [abstract]

2012

Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels.
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 511-524. 2012. Best Paper Honorable Mention Award
[bibtex] [pdf] [presentation]
Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels.
Erik Rodner and Alexander Freytag and Paul Bodesheim and Joachim Denzler.
European Conference on Computer Vision (ECCV). Pages 85-98. 2012.
[bibtex] [pdf] [supplementary]
Divergence-Based One-Class Classification Using Gaussian Processes.
Paul Bodesheim and Erik Rodner and Alexander Freytag and Joachim Denzler.
British Machine Vision Conference (BMVC). Pages 50.1-50.11. 2012. http://dx.doi.org/10.5244/C.26.50
[bibtex] [pdf] [presentation]
Beyond Classification - Large-scale Gaussian Process Inference and Uncertainty Prediction.
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval (NIPS-WS). 2012. This workshop article is a short version abstract of our ACCV'12 paper.
[bibtex] [pdf]

2011

Spectral Clustering of ROIs for Object Discovery.
Paul Bodesheim.
Annual Symposium of the German Association for Pattern Recognition (DAGM). Pages 450-455. 2011.
[bibtex] [pdf] [abstract]