@conference {13106,
	title = {Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2012 - 2019},
	abstract = {Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data.},
	keywords = {(artificial, action, activity, analysis;integer, AND-OR, annotation;video, coding;, constraint;video, construction;semantic, extraction;graph, framework;plots, graph;encoding;human, grounded, intelligence);spatiotemporal, learning, meaning;spatio-temporal, model, phenomena;video, Programming, programming;learning, recognition;human, representation;integer, storyline, theory;image, understanding;visually},
	doi = {10.1109/CVPR.2009.5206492},
	author = {Gupta,A. and Srinivasan,P. and Shi,Jianbo and Davis, Larry S.}
}
