A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Flexible automatic motion blending with registration curves
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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We have focused on the problem of classification of motion frames representing different poses by supervised machine learning and dimensionality reduction techniques. We have extracted motion frames from global database manually, divided them into six different classes and applied classifiers to automatic pose type detection. We have used statistical Bayes, neural network, random forest and Kernel PCA classifiers with wide range of their parameters. We have tried classification on the original data frames and additional reduced their dimensionality by PCA and Kernel PCA methods. We have obtained satisfactory results rated in best case 100 percent of classifiers efficiency.