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» Robust Bayesian Mixture Modelling
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ICASSP
2011
IEEE
13 years 2 months ago
Bayesian sensing hidden Markov models for speech recognition
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
George Saon, Jen-Tzung Chien
ICASSP
2011
IEEE
13 years 2 months ago
Rao-Blackwellized particle filter for Gaussian mixture models and application to visual tracking
One of the most important problems in visual tracking is how to incrementally update the appearance model because the appearance of a target object can be easily changed with time...
Jungho Kim, In-So Kweon
EMMCVPR
1999
Springer
14 years 3 months ago
On Fitting Mixture Models
Consider the problem of tting a nite Gaussian mixture, with an unknown number of components, to observed data. This paper proposes a new minimum description length (MDL) type crite...
Mário A. T. Figueiredo, José M. N. L...
ICML
2003
IEEE
14 years 12 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
ECCV
2006
Springer
14 years 2 months ago
Spatial Segmentation of Temporal Texture Using Mixture Linear Models
In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single tem...
Lee Cooper, Jun Liu, Kun Huang