Levels of details for gaussian mixture models

Authors:
Vincent Garcia;Frank Nielsen;Richard Nock
Affiliations:
Laboratoire d'informatique LIX, Ecole Polytechnique, Palaiseau Cedex, France;Laboratoire d'informatique LIX, Ecole Polytechnique, Palaiseau Cedex, France;CEREGMIA, Université des Antilles-Guyane, Schoelcher, Martinique, France
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

Citing 4
Cited 0

An Efficient Image Similarity Measure Based on Approximations of KL-Divergence Between Two Gaussian Mixtures

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Clustering with Bregman Divergences

The Journal of Machine Learning Research
On Bregman Voronoi diagrams

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Simplifying Mixture Models Using the Unscented Transform

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mixtures of Gaussians are a crucial statistical modeling tool at the heart of many challenging applications in computer vision and machine learning. In this paper, we first describe a novel and efficient algorithm for simplifying Gaussian mixture models using a generalization of the celebrated k-means quantization algorithm tailored to relative entropy. Our method is shown to compare experimentally favourably well with the state-of-the-art both in terms of time and quality performances. Second, we propose a practical enhanced approach providing a hierarchical representation of the simplified GMM while automatically computing the optimal number of Gaussians in the simplified mixture. Application to clustering-based image segmentation is reported.