@conference {12543,
	title = {Learning action dictionaries from video},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1704 - 1707},
	abstract = {Summarizing the contents of a video containing human activities is an important problem in computer vision and has important applications in automated surveillance systems. Summarizing a video requires one to identify and learn a {\textquoteright}vocabulary{\textquoteright} of action-phrases corresponding to specific events and actions occurring in the video. We propose a generative model for dynamic scenes containing human activities as a composition of independent action-phrases - each of which is derived from an underlying vocabulary. Given a long video sequence, we propose a completely unsupervised approach to learn the vocabulary. Once the vocabulary is learnt, a video segment can be decomposed into a collection of phrases for summarization. We then describe methods to learn the correlations between activities and sequentiality of events. We also propose a novel method for building invariances to spatial transforms in the summarization scheme.},
	keywords = {(artificial, action, action-phrases;learning, automated, decomposition;video, dictionaries;spatial, intelligence);video, segment, segmentation;learning, sequence;computer, Surveillance, surveillance;, systems;computer, transforms;video, vision;image, vision;independent},
	doi = {10.1109/ICIP.2008.4712102},
	author = {Turaga,P. and Chellapa, Rama}
}
@article {14889,
	title = {Robust and efficient detection of salient convex groups},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {18},
	year = {1996},
	month = {1996/01//},
	pages = {23 - 37},
	abstract = {This paper describes an algorithm that robustly locates salient convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the gaps between segments is smaller than some fixed proportion of the total length of the lines. This enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise. We give an expected case analysis of the algorithm performance. This demonstrates that salient convexity is unlikely to occur at random, and hence is a strong clue that grouped line segments reflect underlying structure in the scene. We also show that our algorithm run time is O(n <sup>2</sup>log(n)+nm), when we wish to find the m most salient groups in an image with n line segments. We support this analysis with experiments on real data, and demonstrate the grouping system as part of a complete recognition system},
	keywords = {complexity;computer, complexity;contours;image, computational, convex, detection;feature, detection;object, extraction;object, groups;computational, organisation;proximity;salient, recognition;, recognition;line, recognition;perceptual, segment, vision;edge},
	isbn = {0162-8828},
	doi = {10.1109/34.476008},
	author = {Jacobs, David W.}
}
