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Image Distance Using Hidden Markov Models

9 years 7 months ago
Image Distance Using Hidden Markov Models
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its statistical model by an approach related to the dynamic programming used for segmenting Markov chains. Second, given an image segmentation, a statistical model (3D state transition matrix and observation distributions within states) can be estimated. These two steps are repeated until convergence to provide both a segmentation and a statistical model of the image. We propose a statistical distance measure between images based on the similarity of their statistical models, for classification and retrieval tasks.
Daniel DeMenthon, David S. Doermann, Marc Vuilleum
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2000
Where ICPR
Authors Daniel DeMenthon, David S. Doermann, Marc Vuilleumier Stückelberg
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