Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Bayesian hierarchical mixtures of experts
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Shared Kernel Information Embedding for Discriminative Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts
IEEE Transactions on Image Processing
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Discriminative human pose estimation is the problem of inferring the 3D articulated pose of a human directly from an image feature. This is a challenging problem due to the highly non-linear and multi-modal mapping from the image feature space to the pose space. To address this problem, we propose a model employing a mixture of Gaussian processes where each Gaussian process models a local region of the pose space. By employing the models in this way we are able to overcome the limitations of Gaussian processes applied to human pose estimation - their O(N^3) time complexity and their uni-modal predictive distribution. Our model is able to give a multi-modal predictive distribution where each mode is represented by a different Gaussian process prediction. A logistic regression model is used to give a prior over each expert prediction in a similar fashion to previous mixture of expert models. We show that this technique outperforms existing state of the art regression techniques on human pose estimation data sets for ballet dancing, sign language and the HumanEva data set.