Andreetto, Marco and Zelnik-Manor, Lihi and Perona, Pietro (2007) Non-Parametric Probabilistic Image Segmentation. In: IEEE 11th International Conference on Computer Vision. IEEE International Conference on Computer Vision . IEEE , pp. 1104-1111. ISBN 978-1-4244-1630-1 http://resolver.caltech.edu/CaltechAUTHORS:20100813-152900260
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We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks.
|Item Type:||Book Section|
|Additional Information:||© 2007 IEEE. Funding for this research was provided by ONR-MURI Grant N00014-06-1-0734.|
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|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||13 Aug 2010 22:51|
|Last Modified:||26 Dec 2012 12:19|
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