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Paper: IEEE CVPR (2006) “Learning Temporal Sequence Model from Partially Labeled Data”

June 14th, 2006 Irfan Essa Posted in Aaron Bobick, Activity Recognition, Aware Home, Papers, Research, Yifan Shi No Comments »

Yifan Shi, Bobick, A. Essa, I. (2006), “Learning Temporal Sequence Model from Partially Labeled Data” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006
Volume: 2, page(s): 1631 – 1638, ISSN: 1063-6919, ISBN: 0-7695-2597-0, Digital Object Identifier: 10.1109/CVPR.2006.174 [IEEEXplore]

Abstract

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure – the nodes – are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types – vision and inertial measurements – in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.

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Paper: IEEE CVPR (2004) “Propagation networks for recognition of partially ordered sequential action”

June 2nd, 2004 Irfan Essa Posted in Aaron Bobick, Activity Recognition, Aware Home, David Minnen, Papers, Yan Huang, Yifan Shi No Comments »

Yifan Shi, Yan Huang, Minnen, D., Bobick, A., Essa, I. (2004), “Propagation networks for recognition of partially ordered sequential action” In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004 (CVPR 2004). Volume: 2, page(s): II-862 – II-869 Vol.2, ISSN: 1063-6919, ISBN: 0-7695-2158-4, INSPEC Accession Number:8161557, Digital Object Identifier: 10.1109/CVPR.2004.1315255, 27 June-2 July 2004 (IEEEXplore)

Abstract

We present propagation networks (P-nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activity using partially ordered intervals. Each interval is restricted by both temporal and logical constraints, including information about its duration and its temporal relationship with other intervals. P-nets associate one node with each temporal interval. Each node is triggered according to a probability density function that depends on the state of its parent nodes. Each node also has an associated observation function that characterizes supporting perceptual evidence. To facilitate real-time analysis, we introduce a particle filter framework to explore the conditional state space. We modify the original condensation algorithm to more efficiently sample a discrete state space (D-condensation). Experiments in the domain of blood glucose monitor calibration demonstrate both the representational power of P-nets and the effectiveness of the D-condensation algorithm.

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