Irfan Essa’s Academic Activities

Learning Temporal Sequence Model from Partially Labeled Data (IEEEXplore)

Yifan Shi Bobick, A. Essa, I.
Georgia Institute Of Technology, Atalanta
This paper appears in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
Publication Date: 2006
Volume: 2
On page(s): 1631 - 1638
ISSN: 1063-6919
ISBN: 0-7695-2597-0
Digital Object Identifier: 10.1109/CVPR.2006.174
Posted online: 2006-10-09 11:11:21.0

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.

Propagation networks for recognition of partially ordered sequential action (IEEEXplore)

Yifan Shi Yan Huang Minnen, D. Bobick, A. Essa, I.
GVU Center, Georgia Inst. of Technol., Atlanta, GA, USA
This paper appears in: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
Publication Date: 27 June-2 July 2004
Volume: 2
On page(s): II-862 - II-869 Vol.2
Number of Pages: 2001
ISSN: 1063-6919
ISBN: 0-7695-2158-4
INSPEC Accession Number:8161557
Digital Object Identifier: 10.1109/CVPR.2004.1315255
Posted online: 2004-07-19 11:09:30.0

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.