Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hi-index | 0.01 |
In this paper, we propose a novel robust action recognition framework with the following capabilities: 1) online encoding motions to multi-label sequence where the output in each frame is a tuple of labels rather than a single label, 2) providing efficient automatic relevant motion selection framework, 3) learning systems so as to be optimal for online multi-label sequence classification. As for multi-label classification, our approach incorporates contextual information about action not only temporal information but hierarchical information of actions. Inference tends to be complex so as to achieve such complex recognition scheme, however, we propose an efficient Viterbi-like decoding algorithm which integrates forward algorithm and loopy message passing algorithm. As for the learning process, the algorithm optimizes the parameters so as to maximize log likelihood of the model. Boosting, ensemble approach of machine learning, is leveraged to provide efficient feature selection framework in the training process. The experimental results show that the proposed method successfully exploits the impact of contextual information then significantly outperforms the traditional approaches in dynamic gait motion classification.