Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multi-Scale Gesture Recognition from Time-Varying Contours
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Avoiding the "Streetlight Effect": Tracking by Exploring Likelihood Modes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Conditional Random Fields for Contextual Human Motion Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate and efficient gesture spotting via pruning and subgesture reasoning
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Can our TV robustly understand human gestures?: real-time gesture localization in range data
Proceedings of the 9th European Conference on Visual Media Production
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In this paper, we present a novel technique for classifying multimodal temporal events. Our main contribution is the introduction of temporal random forests (TRFs), an extension of random forests (and decision trees in general) to the time domain. The approach is relatively simple and able to discriminatively learn event classes while performing feature selection in an implicit fashion. We describe here our ongoing research and present experiments performed on gesture and audio-visual speech recognition datasets comparing our method against state-of-the-art algorithms.