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CVPR
2012
IEEE
7 years 9 months ago
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors ...
Bolei Zhou, Xiaogang Wang, Xiaoou Tang
BMCBI
2011
8 years 10 months ago
A Beta-Mixture Model for Dimensionality Reduction, Sample Classification and Analysis
Background: Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper ...
Kirsti Laurila, Bodil Oster, Claus L. Andersen, Ph...
ICASSP
2011
IEEE
8 years 10 months ago
Non-parallel training for voice conversion based on FT-GMM
This paper presents a non-parallel training algorithm for voice conversion based on feature transform Gaussian mixture model (FTGMM), which is a mixture model of joint density spa...
Ling-Hui Chen, Zhen-Hua Ling, Li-Rong Dai
TCSV
2010
9 years 1 months ago
Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models
Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extractin...
Harish Bhaskar, Lyudmila Mihaylova, Alin Achim
INTERSPEECH
2010
9 years 1 months ago
Boosted mixture learning of Gaussian mixture HMMs for speech recognition
In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models fo...
Jun Du, Yu Hu, Hui Jiang
PAMI
2011
9 years 1 months ago
Rigid and Articulated Point Registration with Expectation Conditional Maximization
—This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unkno...
Radu Horaud, Florence Forbes, Manuel Yguel, Guilla...
TASLP
2010
138views more  TASLP 2010»
9 years 5 months ago
Source/Filter Model for Unsupervised Main Melody Extraction From Polyphonic Audio Signals
— Extracting the main melody from a polyphonic music recording seems natural even to untrained human listeners. To a certain extent it is related to the concept of source separat...
Jean-Louis Durrieu, Gaël Richard, Bertrand Da...
JCST
2010
139views more  JCST 2010»
9 years 5 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
PAMI
2002
114views more  PAMI 2002»
9 years 6 months ago
Unsupervised Learning of Finite Mixture Models
Mário A. T. Figueiredo, Anil K. Jain
JCB
2007
191views more  JCB 2007»
9 years 6 months ago
Bayesian Haplotype Inference via the Dirichlet Process
The problem of inferring haplotypes from genotypes of single nucleotide polymorphisms (SNPs) is essential for the understanding of genetic variation within and among populations, ...
Eric P. Xing, Michael I. Jordan, Roded Sharan
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