Bayesian order-adaptive clustering for video segmentation

Authors:
Peter Orbanz;Samuel Braendle;Joachim M. Buhmann
Affiliations:
Institute of Computational Science, ETH Zurich;Institute of Computational Science, ETH Zurich;Institute of Computational Science, ETH Zurich
Venue:
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Year:
2007

Citing 5
Cited 1

Learning and Recognizing Human Dynamics in Video Sequences

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Unified Mixture Framework for Motion Segmentation: Incorporating Spatial Coherence and Estimating the Number of Models

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Stability-based validation of clustering solutions

Neural Computation
Context-Based Segmentation of Image Sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Bayesian Image Segmentation

International Journal of Computer Vision

Online video segmentation by bayesian split-merge clustering

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 clustering problem. Context information is propagated over time by a conjugate structure. The level of segment resolution is controlled by a Dirichlet process prior. Our contributions include a conjugate nonparametric Bayesian model for clustering in multivariate time series, a MCMC inference algorithm, and a multiscale sampling approach for Dirichlet process mixture models. The multiscale algorithm is applicable to data with a spatial structure. The method is tested on synthetic data and on videos from the MPEG4 benchmark set.