Spatio-temporal extraction of articulated models in a graph pyramid
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Automated detection of major thoracic structures with a novel online learning method
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Hierarchical spatio-temporal extraction of models for moving rigid parts
Pattern Recognition Letters
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
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Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in Forests can be replaced by even simpler structures, e.g., Random Naive Bayes classifiers, yielding similar performance. The goal of this paper is to benefit from these findings to develop an efficient on-line learner. Based on the principals of on-line Random Forests, we adapt the Random Naive Bayes classifier to the on-line domain. For that purpose, we propose to use on-line histograms as weak learners, which yield much better performance than simple decision stumps. Experimentally we show, that the approach is applicable to incremental learning on machine learning datasets. Additionally, we propose to use an iir filtering-like forgetting function for the weak learners to enable adaptivity and evaluate our classifier on the task of tracking by detection.