Hidden Markov Models for Images
نویسندگان
چکیده
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. We show that the Viterbi algorithm approach used for segmenting Markov chains can be extended to Markov meshes. The segmental k-means algorithm can then be applied to iteratively estimate the state transition matrix and the probability densities of the observations for the model. We also describe a semi-Markov modeling technique in which the distributions of width and heights of the segmented regions are modeled explicitly. Finally, we propose a distance measure between images based on the similarity of their statistical models, for classification and retrieval tasks. The support of this research by the Department of Defense under contract MDA 9049-6C-1250 is gratefully acknowledged.
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