Image retrieval using color histograms generated by Gauss mixture vector quantization
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Convolution on the n-sphere with application to PDF modeling
IEEE Transactions on Signal Processing
A system for online power prediction in virtualized environments using Gaussian mixture models
Proceedings of the 47th Design Automation Conference
A novel video thumbnail extraction method using spatiotemporal vector quantization
Proceedings of the 3rd international workshop on Automated information extraction in media production
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Gauss mixtures are a popular class of models in statistics and statistical signal processing because they can provide good fits to smooth densities, because they have a rich theory, and because they can be well estimated by existing algorithms such as the EM (expectation maximization) algorithm. We here extend an information theoretic extremal property for source coding from Gaussian sources to Gauss mixtures using high rate quantization theory and extend a method originally used for LPC (linear predictive coding) speech vector quantization to provide a Lloyd clustering approach to the design of Gauss mixture models. The theory provides formulas relating minimum discrimination information (MDI) for model selection and the mean squared error resulting when the MDI criterion is used in an optimized robust classified vector quantizer. It also provides motivation for the use of Gauss mixture models for robust compression systems for general random vectors.