Uncertainty in Artificial Intelligence
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Learning Graphical Models of Images, Videos and Their Spatial Transformations
Brendan Frey, Nebojsa Jojic
Abstract:
Mixtures of Gaussians, factor analyzers (probabilistic PCA) and hidden Markov models are staples of static and dynamic data modeling and image and video modeling in particular. We show how topographic transformations in the input, such as translation and shearing in images, can be accounted for in these models by including a discrete transformation variable. The resulting models perform clustering, dimensionality reduction and time-series analysis in a way that is invariant to transformations in the input. Using the EM algorithm, these transformation-invariant models can be fit to static data and time series. We give results on filtering microscopy images, face and facial pose clustering, handwritten digit modeling and recognition, video clustering, object tracking, and removal of distractions from video sequences.
Keywords: dynamic graphical model, vision, video summary, video, motion estimation, stabilizati
Pages: 184-191
PS Link: http://www.cs.toronto.edu/~frey/papers/gmiv-uai00.ps
PDF Link: /papers/00/p184-frey.pdf
BibTex:
@INPROCEEDINGS{Frey00,
AUTHOR = "Brendan Frey and Nebojsa Jojic",
TITLE = "Learning Graphical Models of Images, Videos and Their Spatial Transformations",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "184--191"
}


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