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- Peter Orbanz, Joachim M. Buhmann
- International Journal of Computer Vision
- 2007

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogramâ€¦ (More)

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations betweenâ€¦ (More)

- Sinead Williamson, Peter Orbanz, Zoubin Ghahramani
- AISTATS
- 2010

Latent variable models represent hidden structure in observational data. To account for the distribution of the observational data changing over time, space or some other covariate, we needâ€¦ (More)

- Peter Orbanz, Yee Whye Teh
- Encyclopedia of Machine Learning and Data Mining
- 2010

A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learningâ€¦ (More)

- Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann
- ICML
- 2007

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measuredâ€¦ (More)

- Peter Orbanz, Joachim M. Buhmann
- ECCV
- 2006

A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assignments. Theâ€¦ (More)

- Peter Orbanz
- NIPS
- 2009

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem hasâ€¦ (More)

- Peter Orbanz, Daniel M. Roy
- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2015

The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finettiâ€™s theorem provides the theoretical foundation. Dirichlet processâ€¦ (More)

- Benjamin Bloem-Reddy, Peter Orbanz
- ArXiv
- 2017

We study preferential attachment mechanisms in random graphs that are parameterized by (i) a constant bias affecting the degreebiased distribution on the vertex set and (ii) the distribution of timesâ€¦ (More)

- Peter Orbanz, Samuel Braendle, Joachim M. Buhmann
- EMMCVPR
- 2007

Video segmentation requires the partitioning of a series of images into groups that are both spatially coherent and smooth along the time axis. We formulate segmentation as a Bayesian clusteringâ€¦ (More)