@article {14430,
	title = {Dynamic Processing Allocation in Video},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {33},
	year = {2011},
	month = {2011/11//},
	pages = {2174 - 2187},
	abstract = {Large stores of digital video pose severe computational challenges to existing video analysis algorithms. In applying these algorithms, users must often trade off processing speed for accuracy, as many sophisticated and effective algorithms require large computational resources that make it impractical to apply them throughout long videos. One can save considerable effort by applying these expensive algorithms sparingly, directing their application using the results of more limited processing. We show how to do this for retrospective video analysis by modeling a video using a chain graphical model and performing inference both to analyze the video and to direct processing. We apply our method to problems in background subtraction and face detection, and show in experiments that this leads to significant improvements over baseline algorithms.},
	keywords = {algorithms;digital, allocation;video, analysis;computer, background, detection;graphical, detection;resource, graphics;face, model;resource, processing;, processing;face, recognition;object, signal, subtraction;baseline, video},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.55},
	author = {Daozheng Chen and Bilgic,M. and Getoor, Lise and Jacobs, David W.}
}
@conference {12551,
	title = {Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.},
	keywords = {algorithm;learning, analysis;computer, analysis;statistical, analysis;video, based, classification;image, classification;spatio-temporal, distribution, distributions;, Face, functions;shape, Grassmann, invariant, manifold;activity, manifold;Stiefel, matching;inference, matching;spatiotemporal, measures;estimation, modeling;statistical, parameters;pattern, phenomena;statistical, recognition;affine, recognition;computer, recognition;probability, SHAPE, structure;image, technique;geometric, theory;manifold-valued, vision;distance, vision;image},
	doi = {10.1109/CVPR.2008.4587733},
	author = {Turaga,P. and Veeraraghavan,A. and Chellapa, Rama}
}
