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ACCV
2009
Springer

Levels of Details for Gaussian Mixture Models

9 years 1 months ago
Levels of Details for Gaussian Mixture Models
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.
Vincent Garcia, Frank Nielsen, Richard Nock
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACCV
Authors Vincent Garcia, Frank Nielsen, Richard Nock
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