TY - CONF
AB - In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foundation it is hard to optimize. We propose an approximate optimization formulation that leads to an efficient algorithm with low runtime complexity. The algorithm has a single free parameter, the number of clusters to find. We demonstrate superior performance on several synthetic and real datasets.
AU - MÃ¼ller, Andreas
AU - Nowozin, Sebastian
AU - Lampert, Christoph
ID - 3126
TI - Information theoretic clustering using minimal spanning trees
VL - 7476
ER -
TY - CONF
AB - When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques.
We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data, we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations.
AU - Quadrianto, Novi
AU - Lampert, Christoph
AU - Chen, Chao
ID - 3127
T2 - Proceedings of the 29th International Conference on Machine Learning
TI - The most persistent soft-clique in a set of sampled graphs
ER -
TY - JOUR
AB - Overview of the Special Issue on structured prediction and inference.
AU - Blaschko, Matthew
AU - Lampert, Christoph
ID - 3164
IS - 3
JF - International Journal of Computer Vision
TI - Guest editorial: Special issue on structured prediction and inference
VL - 99
ER -
TY - JOUR
AB - We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames/s. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz.
AU - Lampert, Christoph
AU - Peters, Jan
ID - 3248
IS - 1
JF - Journal of Real-Time Image Processing
TI - Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components
VL - 7
ER -
TY - GEN
AB - We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks.
AU - Chen, Chao
AU - Freedman, Daniel
AU - Lampert, Christoph
ID - 5386
SN - 2664-1690
TI - Enforcing topological constraints in random field image segmentation
ER -