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Displaying 1-10 out of 10 total
A Non-parametric Bayesian Network Prior of Human Pose
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Andreas M. Lehrmann,Peter V. Gehler,Sebastian Nowozin
Issue Date:December 2013
pp. 1281-1288
Having a sensible prior of human pose is a vital ingredient for many computer vision applications, including tracking and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the ...
Decision tree fields
Found in: Computer Vision, IEEE International Conference on
By Sebastian Nowozin,Carsten Rother,Shai Bagon,Toby Sharp, Bangpeng Yao,Pushmeet Kohli
Issue Date:November 2011
pp. 1668-1675
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF mode...
Frequent Subgraph Retrieval in Geometric Graph Databases
Found in: Data Mining, IEEE International Conference on
By Sebastian Nowozin, Koji Tsuda
Issue Date:December 2008
pp. 953-958
Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates.
Discriminative Subsequence Mining for Action Classification
Found in: Computer Vision, IEEE International Conference on
By Sebastian Nowozin, Gokhan Bakir, Koji Tsuda
Issue Date:October 2007
pp. 1-8
Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But t...
Weighted Substructure Mining for Image Analysis
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo, Gokhan Bakir
Issue Date:June 2007
pp. 1-8
In web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and the linear support vector machine, one can partly fulfill the goal, but the ac...
Image Segmentation UsingHigher-Order Correlation Clustering
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Sungwoong Kim,Chang D. Yoo,Sebastian Nowozin,Pushmeet Kohli
Issue Date:September 2014
pp. 1761-1774
In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider sele...
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Jorg H. Kappes,Bjoern Andres,Fred A. Hamprecht,Christoph Schnorr,Sebastian Nowozin,Dhruv Batra,Sungwoong Kim,Bernhard X. Kausler,Jan Lellmann,Nikos Komodakis,Carsten Rother
Issue Date:June 2013
pp. 1328-1335
Even years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these ins...
Discriminative Non-blind Deblurring
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Uwe Schmidt,Carsten Rother,Sebastian Nowozin,Jeremy Jancsary,Stefan Roth
Issue Date:June 2013
pp. 604-611
Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this dat...
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Sebastian Nowozin, Stefanie Jegelka
Issue Date:June 2009
pp. 1-8
We propose a new method to quantify the solution stability of a large class of combinatorial optimization problems arising in machine learning. As practical example we apply the method to correlation clustering, clustering aggregation, modularity clusterin...
A decoupled approach to exemplar-based unsupervised learning
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Gokhan Bakir, Sebastian Nowozin
Issue Date:July 2008
pp. 704-711
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possible prototypes to training exemplars. In particular, this has been done for c...