Comments on Approximating Discrete Probability Distributions with Dependence Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with mixtures of trees
The Journal of Machine Learning Research
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A constraint-based genetic algorithm approach for mining classification rules
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper presents a new method to label parts of human body automatically based on the joint probability density function (PDF). To adapt to different motion for different articulation, the probabilistic models of each triangle different number of mixture components with MML are adopted. To solve the computation load problem of genetic algorithm (GA), a constraint-based genetic algorithm (CBGA) is developed to obtain the best global labeling. Our algorithm is developed to report the performance with experiments from running, walking and dancing sequences.