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2013


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Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity

Schultz, T., Gröschel, S.

In Proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, pages: 663-670, (Editors: K Mori and I Sakuma and Y Sato and C Barillot and N Navab), Springer, MICCAI, 2013, Lecture Notes in Computer Science, vol. 8149 (inproceedings)

DOI [BibTex]

2013

DOI [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

Web [BibTex]

Web [BibTex]


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Learning Sequential Motor Tasks

Daniel, C., Neumann, G., Peters, J.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


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Information-Theoretic Motor Skill Learning

Neumann, G., Kupcsik, A., Deisenroth, M., Peters, J.

In Proceedings of the 27th AAAI 2013, Workshop on Intelligent Robotic Systems (AAAI 2013), 2013 (inproceedings)

[BibTex]

[BibTex]


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Measuring Statistical Dependence via the Mutual Information Dimension

Sugiyama, M., Borgwardt, KM.

In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pages: 1692-1698, (Editors: Francesca Rossi), AAAI Press, Menlo Park, California, IJCAI, 2013 (inproceedings)

[BibTex]

[BibTex]


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Analytical probabilistic proton dose calculation and range uncertainties

Bangert, M., Hennig, P., Oelfke, U.

In 17th International Conference on the Use of Computers in Radiation Therapy, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

[BibTex]

[BibTex]


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Adaptivity to Local Smoothness and Dimension in Kernel Regression

Kpotufe, S., Garg, V.

In Advances in Neural Information Processing Systems 26, pages: 3075-3083, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators

Besserve, M., Logothetis, N., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 2535-2543, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Rakitsch, B., Lippert, C., Borgwardt, KM., Stegle, O.

In Advances in Neural Information Processing Systems 26, pages: 1466-1474, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Model-based Imitation Learning by Probabilistic Trajectory Matching

Englert, P., Paraschos, A., Peters, J., Deisenroth, M.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pages: 1922-1927, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Towards neurofeedback for improving visual attention

Zander, T., Battes, B., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, pages: Article ID: 086, (Editors: J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins), Verlag der Technischen Universität Graz, 5th International Brain-Computer Interface Meeting, 2013, Article ID: 086 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions

Jung, M., Zscheischler, J.

In Proceedings of the International Conference on Computational Science, 18, pages: 2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

PDF [BibTex]

PDF [BibTex]


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Learning responsive robot behavior by imitation

Ben Amor, H., Vogt, D., Ewerton, M., Berger, E., Jung, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pages: 3257-3264, IEEE, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Learning Skills with Motor Primitives

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Proceedings of the 16th Yale Workshop on Adaptive and Learning Systems, 2013 (inproceedings)

[BibTex]

[BibTex]


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Scalable Influence Estimation in Continuous-Time Diffusion Networks

Du, N., Song, L., Gomez Rodriguez, M., Zha, H.

In Advances in Neural Information Processing Systems 26, pages: 3147-3155, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Rapid Distance-Based Outlier Detection via Sampling

Sugiyama, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 467-475, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Probabilistic Movement Primitives

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 26, pages: 2616-2624, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Causal Inference on Time Series using Restricted Structural Equation Models

Peters, J., Janzing, D., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Regression-tree Tuning in a Streaming Setting

Kpotufe, S., Orabona, F.

In Advances in Neural Information Processing Systems 26, pages: 1788-1796, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Density estimation from unweighted k-nearest neighbor graphs: a roadmap

von Luxburg, U., Alamgir, M.

In Advances in Neural Information Processing Systems 26, pages: 225-233, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


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Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

[BibTex]


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Automatic Malaria Diagnosis system

Mehrjou, A., Abbasian, T., Izadi, M.

In First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pages: 205-211, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

link (url) [BibTex]

link (url) [BibTex]


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Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

link (url) [BibTex]

link (url) [BibTex]

2010


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Learning Table Tennis with a Mixture of Motor Primitives

Mülling, K., Kober, J., Peters, J.

In Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010), pages: 411-416, IEEE, Piscataway, NJ, USA, 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), December 2010 (inproceedings)

Abstract
Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate control, and online adaptation. To represent the elementary movements needed for robot table tennis, we rely on dynamic systems motor primitives (DMP). While such DMPs have been successfully used for learning a variety of simple motor tasks, they only represent single elementary actions. In order to select and generalize among different striking movements, we present a new approach, called Mixture of Motor Primitives that uses a gating network to activate appropriate motor primitives. The resulting policy enables us to select among the appropriate motor primitives as well as to generalize between them. In order to obtain a fully learned robot table tennis setup, we also address the problem of predicting the necessary context information, i.e., the hitting point in time and space where we want to hit the ball. We show that the resulting setup was capable of playing rudimentary table tennis using an anthropomorphic robot arm.

Web DOI [BibTex]

2010

Web DOI [BibTex]


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Learning an interactive segmentation system

Nickisch, H., Rother, C., Kohli, P., Rhemann, C.

In Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010), pages: 274-281, (Editors: Chellapa, R. , P. Anandan, A. N. Rajagopalan, P. J. Narayanan, P. Torr), ACM Press, Nw York, NY, USA, Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), December 2010 (inproceedings)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user -- a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy

Bangert, M., Hennig, P., Oelfke, U.

In pages: 746-751 , (Editors: Draghici, S. , T.M. Khoshgoftaar, V. Palade, W. Pedrycz, M.A. Wani, X. Zhu), IEEE, Piscataway, NJ, USA, Ninth International Conference on Machine Learning and Applications (ICMLA), December 2010 (inproceedings)

Abstract
We present a method for fully automated selection of treatment beam ensembles for external radiation therapy. We reformulate the beam angle selection problem as a clustering problem of locally ideal beam orientations distributed on the unit sphere. For this purpose we construct an infinite mixture of von Mises-Fisher distributions, which is suited in general for density estimation from data on the D-dimensional sphere. Using a nonparametric Dirichlet process prior, our model infers probability distributions over both the number of clusters and their parameter values. We describe an efficient Markov chain Monte Carlo inference algorithm for posterior inference from experimental data in this model. The performance of the suggested beam angle selection framework is illustrated for one intra-cranial, pancreas, and prostate case each. The infinite von Mises-Fisher mixture model (iMFMM) creates between 18 and 32 clusters, depending on the patient anatomy. This suggests to use the iMFMM directly for beam ensemble selection in robotic radio surgery, or to generate low-dimensional input for both subsequent optimization of trajectories for arc therapy and beam ensemble selection for conventional radiation therapy.

Web DOI [BibTex]

Web DOI [BibTex]


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Online algorithms for submodular minimization with combinatorial constraints

Jegelka, S., Bilmes, J.

In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning: Structures, Algorithms and Applications (DISCML), December 2010 (inproceedings)

Abstract
Building on recent results for submodular minimization with combinatorial constraints, and on online submodular minimization, we address online approximation algorithms for submodular minimization with combinatorial constraints. We discuss two types of algorithms and outline approximation algorithms that integrate into those.

PDF Web [BibTex]

PDF Web [BibTex]


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Multi-agent random walks for local clustering

Alamgir, M., von Luxburg, U.

In Proceedings of the IEEE International Conference on Data Mining (ICDM 2010), pages: 18-27, (Editors: Webb, G. I., B. Liu, C. Zhang, D. Gunopulos, X. Wu), IEEE, Piscataway, NJ, USA, IEEE International Conference on Data Mining (ICDM), December 2010 (inproceedings)

Abstract
We consider the problem of local graph clustering where the aim is to discover the local cluster corresponding to a point of interest. The most popular algorithms to solve this problem start a random walk at the point of interest and let it run until some stopping criterion is met. The vertices visited are then considered the local cluster. We suggest a more powerful alternative, the multi-agent random walk. It consists of several “agents” connected by a fixed rope of length l. All agents move independently like a standard random walk on the graph, but they are constrained to have distance at most l from each other. The main insight is that for several agents it is harder to simultaneously travel over the bottleneck of a graph than for just one agent. Hence, the multi-agent random walk has less tendency to mistakenly merge two different clusters than the original random walk. In our paper we analyze the multi-agent random walk theoretically and compare it experimentally to the major local graph clustering algorithms from the literature. We find that our multi-agent random walk consistently outperforms these algorithms.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Effects of Packet Losses to Stability in Bilateral Teleoperation Systems

Hong, A., Cho, JH., Lee, DY.

In pages: 1043-1044, Korean Society of Mechanical Engineers, Seoul, South Korea, KSME Fall Annual Meeting, November 2010 (inproceedings)

[BibTex]

[BibTex]


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Combining Real-Time Brain-Computer Interfacing and Robot Control for Stroke Rehabilitation

Gomez Rodriguez, M., Peters, J., Hill, J., Gharabaghi, A., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of SIMPAR 2010 Workshops, pages: 59-63, Brain-Computer Interface Workshop at SIMPAR: 2nd International Conference on Simulation, Modeling, and Programming for Autonomous Robots, November 2010 (inproceedings)

Abstract
Brain-Computer Interfaces based on electrocorticography (ECoG) or electroencephalography (EEG), in combination with robot-assisted active physical therapy, may support traditional rehabilitation procedures for patients with severe motor impairment due to cerebrovascular brain damage caused by stroke. In this short report, we briefly review the state-of-the art in this exciting new field, give an overview of the work carried out at the Max Planck Institute for Biological Cybernetics and the University of T{\"u}bingen, and discuss challenges that need to be addressed in order to move from basic research to clinical studies.

PDF Web [BibTex]

PDF Web [BibTex]


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Comparative Quantitative Evaluation of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

Mantlik, F., Hofmann, M., Bezrukov, I., Kolb, A., Beyer, T., Reimold, M., Pichler, B., Schölkopf, B.

2010(M08-4), 2010 Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), November 2010 (talk)

Abstract
Combined PET/MR provides at the same time molecular and functional imaging as well as excellent soft tissue contrast. It does not allow one to directly measure the attenuation properties of scanned tissues, despite the fact that accurate attenuation maps are necessary for quantitative PET imaging. Several methods have therefore been proposed for MR-based attenuation correction (MR-AC). So far, they have only been evaluated on data acquired from separate MR and PET scanners. We evaluated several MR-AC methods on data from 10 patients acquired on a combined BrainPET/MR scanner. This allowed the consideration of specific PET/MR issues, such as the RF coil that attenuates and scatters 511 keV gammas. We evaluated simple MR thresholding methods as well as atlas and machine learning-based MR-AC. CT-based AC served as gold standard reference. To comprehensively evaluate the MR-AC accuracy, we used RoIs from 2 anatomic brain atlases with different levels of detail. Visual inspection of the PET images indicated that even the basic FLASH threshold MR-AC may be sufficient for several applications. Using a UTE sequence for bone prediction in MR-based thresholding occasionally led to false prediction of bone tissue inside the brain, causing a significant overestimation of PET activity. Although it yielded a lower mean underestimation of activity, it exhibited the highest variance of all methods. The atlas averaging approach had a smaller mean error, but showed high maximum overestimation on the RoIs of the more detailed atlas. The Nave Bayes and Atlas-Patch MR-AC yielded the smallest variance, and the Atlas-Patch also showed the smallest mean error. In conclusion, Atlas-based AC using only MR information on the BrainPET/MR yields a high level of accuracy that is sufficient for clinical quantitative imaging requirements. The Atlas-Patch approach was superior to alternative atlas-based methods, yielding a quantification error below 10% for all RoIs except very small ones.

[BibTex]

[BibTex]


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Learning as a key ability for Human-Friendly Robots

Peters, J., Kober, J., Mülling, K., Krömer, O., Nguyen-Tuong, D., Wang, Z., Rodriguez Gomez, M., Grosse-Wentrup, M.

In pages: 1-2, 3rd Workshop for Young Researchers on Human-Friendly Robotics (HFR), October 2010 (inproceedings)

Web [BibTex]

Web [BibTex]


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Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.

In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pages: 121-126, IEEE, Piscataway, NJ, USA, IEEE International Conference on Systems, Man and Cybernetics (SMC), October 2010 (inproceedings)

Abstract
Brain-Computer Interfaces (BCIs) in combination with robot-assisted physical therapy may become a valuable tool for neurorehabilitation of patients with severe hemiparetic syndromes due to cerebrovascular brain damage (stroke) and other neurological conditions. A key aspect of this approach is reestablishing the disrupted sensorimotor feedback loop, i.e., determining the intended movement using a BCI and helping a human with impaired motor function to move the arm using a robot. It has not been studied yet, however, how artificially closing the sensorimotor feedback loop affects the BCI decoding performance. In this article, we investigate this issue in six healthy subjects, and present evidence that haptic feedback facilitates the decoding of arm movement intention. The results provide evidence of the feasibility of future rehabilitative efforts combining robot-assisted physical therapy with BCIs.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., Peters, J.

In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1586-1591, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

Abstract
This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical grasp configurations from visual descriptors that are associated with the contours of an object. While these hypothetical configurations are abundant, labeled configurations are very scarce as these are acquired via time-costly experiments carried out by the robot. Kernel logistic regression (KLR) via joint kernel maps is trained to map the hypothesis space of grasps into continuous class-conditional probability values indicating their achievability. We propose a soft-supervised extension of KLR and a framework to combine the merits of semi-supervised and active learning approaches to tackle the scarcity of labeled grasps. Experimental evaluation shows that combining active and semi-supervised learning is favorable in the existence of an oracle. Furthermore, semi-supervised learning outperforms supervised learning, particularly when the labeled data is very limited.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A biomimetic approach to robot table tennis

Mülling, K., Kober, J., Peters, J.

In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1921-1926, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

Abstract
Although human beings see and move slower than table tennis or baseball robots, they manage to outperform such robot systems. One important aspect of this better performance is the human movement generation. In this paper, we study trajectory generation for table tennis from a biomimetic point of view. Our focus lies on generating efficient stroke movements capable of mastering variations in the environmental conditions, such as changing ball speed, spin and position. We study table tennis from a human motor control point of view. To make headway towards this goal, we construct a trajectory generator for a single stroke using the discrete movement stages hypothesis and the virtual hitting point hypothesis to create a model that produces a human-like stroke movement. We verify the functionality of the trajectory generator for a single forehand stroke both in a simulation and using a real Barrett WAM.

Web DOI [BibTex]

Web DOI [BibTex]


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Bayesian Inference and Experimental Design for Large Generalised Linear Models

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

PDF Web [BibTex]

PDF Web [BibTex]


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Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning

Lampert, C., Kroemer, O.

In Computer Vision – ECCV 2010, pages: 566-579, (Editors: Daniilidis, K. , P. Maragos, N. Paragios), Springer, Berlin, Germany, 11th European Conference on Computer Vision, September 2010 (inproceedings)

Abstract
We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Simple algorithmic modifications for improving blind steganalysis performance

Schwamberger, V., Franz, M.

In Proceedings of the 12th ACM workshop on Multimedia and Security (MM&Sec 2010), pages: 225-230, (Editors: Campisi, P. , J. Dittmann, S. Craver), ACM Press, New York, NY, USA, 12th ACM Workshop on Multimedia and Security (MM&Sec), September 2010 (inproceedings)

Abstract
Most current algorithms for blind steganalysis of images are based on a two-stages approach: First, features are extracted in order to reduce dimensionality and to highlight potential manipulations; second, a classifier trained on pairs of clean and stego images finds a decision rule for these features to detect stego images. Thereby, vector components might vary significantly in their values, hence normalization of the feature vectors is crucial. Furthermore, most classifiers contain free parameters, and an automatic model selection step has to be carried out for adapting these parameters. However, the commonly used cross-validation destroys some information needed by the classifier because of the arbitrary splitting of image pairs (stego and clean version) in the training set. In this paper, we propose simple modifications of normalization and for standard cross-validation. In our experiments, we show that these methods lead to a significant improvement of the standard blind steganalyzer of Lyu and Farid.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Semi-supervised Remote Sensing Image Classification via Maximum Entropy

Erkan, A., Camps-Valls, G., Altun, Y.

In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages: 313-318, IEEE, Piscataway, NJ, USA, 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2010 (inproceedings)

Abstract
Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples. While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and/or applicability to multi-categorical data. In this paper, we evaluate semi-supervised logistic regression (SLR), a recent information theoretic semi-supervised algorithm, for remote sensing image classification problems. SLR is a probabilistic discriminative classifier and a specific instance of the generalized maximum entropy framework with a convex loss function. Moreover, the method is inherently multi-class and easy to implement. These characteristics make SLR a strong alternative to the widely used semi-supervised variants of SVM for the segmentation of remote sensing images. We demonstrate the competitiveness of SLR in multispectral, hyperspectral and radar image classifica tion.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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MLSP Competition, 2010: Description of first place method

Leiva, JM., Martens, SMM.

In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages: 112-113, IEEE, Piscataway, NJ, USA, 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2010 (inproceedings)

Abstract
Our winning approach to the 2010 MLSP Competition is based on a generative method for P300-based BCI decoding, successfully applied to visual spellers. Here, generative has a double meaning. On the one hand, we work with a probability density model of the data given the target/non target labeling, as opposed to discriminative (e.g. SVM-based) methods. On the other hand, the natural consequence of this approach is a decoding based on comparing the observation to templates generated from the data.

Web DOI [BibTex]

Web DOI [BibTex]


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Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM

Harmeling, S., Sra, S., Hirsch, M., Schölkopf, B.

In Proceedings of the 17th International Conference on Image Processing (ICIP 2010), pages: 3313-3316, IEEE, Piscataway, NJ, USA, 17th International Conference on Image Processing (ICIP), September 2010 (inproceedings)

Abstract
We formulate the multiframe blind deconvolution problem in an incremental expectation maximization (EM) framework. Beyond deconvolution, we show how to use the same framework to address: (i) super-resolution despite noise and unknown blurring; (ii) saturationcorrection of overexposed pixels that confound image restoration. The abundance of data allows us to address both of these without using explicit image or blur priors. The end result is a simple but effective algorithm with no hyperparameters. We apply this algorithm to real-world images from astronomy and to super resolution tasks: for both, our algorithm yields increased resolution and deconvolved images simultaneously.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Nickisch, H., Rasmussen, C.

In Pattern Recognition, pages: 271-282, (Editors: Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler), Springer, Berlin, Germany, 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM), September 2010 (inproceedings)

Abstract
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.

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PDF Web DOI [BibTex]


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A Nearest Neighbor Data Structure for Graphics Hardware

Cayton, L.

In Proceedings of the First International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS 2010), pages: 1-6, First International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS), September 2010 (inproceedings)

Abstract
Nearest neighbor search is a core computational task in database systems and throughout data analysis. It is also a major computational bottleneck, and hence an enormous body of research has been devoted to data structures and algorithms for accelerating the task. Recent advances in graphics hardware provide tantalizing speedups on a variety of tasks and suggest an alternate approach to the problem: simply run brute force search on a massively parallel sys- tem. In this paper we marry the approaches with a novel data structure that can effectively make use of parallel systems such as graphics cards. The architectural complexities of graphics hardware - the high degree of parallelism, the small amount of memory relative to instruction throughput, and the single instruction, multiple data design- present significant challenges for data structure design. Furthermore, the brute force approach applies perfectly to graphics hardware, leading one to question whether an intelligent algorithm or data structure can even hope to outperform this basic approach. Despite these challenges and misgivings, we demonstrate that our data structure - termed a Random Ball Cover - provides significant speedups over the GPU- based brute force approach.

PDF Web [BibTex]

PDF Web [BibTex]