Density estimation using mixtures of mixtures of gaussians

Authors:
Wael Abd-Almageed;Larry S. Davis
Affiliations:
Institute for Advanced Computer Studies, University of Maryland, College Park, MD;Institute for Advanced Computer Studies, University of Maryland, College Park, MD
Venue:
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Year:
2006

Citing 5
Cited 1

Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference in model-based cluster analysis

Statistics and Computing
Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Improved Fast Gauss Transform and Efficient Kernel Density Estimation

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

Robust appearance modeling for pedestrian and vehicle tracking

CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new density estimation algorithm using mixtures of mixtures of Gaussians. The new algorithm overcomes the limitations of the popular Expectation Maximization algorithm. The paper first introduces a new model selection criterion called the Penalty-less Information Criterion, which is based on the Jensen-Shannon divergence. Mean-shift is used to automatically initialize the means and covariances of the Expectation Maximization in order to obtain better structure inference. Finally, a locally linear search is performed using the Penalty-less Information Criterion in order to infer the underlying density of the data. The validity of the algorithm is verified using real color images.