A Multigrid Approach to the Gibbsian Classification of Mammograms
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
ITCH: information-theoretic cluster hierarchies
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Genetic algorithm for finding cluster hierarchies
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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This work takes place in the context of hierarchical stochastic models for the resolution of discrete inverse problems from low level vision. Some of these models lie on the nodes of a quad-tree which leads to non-iterative inference procedures. Nevertheless, if they circumvent the algorithmic drawbacks of grid-based models (computational load and/or great dependence on the initialization), they admit modeling shortcomings (cumbersome and somehow artificial). We investigate a new hierarchical stochastic model which takes benefit from both the spatial and the hierarchical prior modelings. The independence graph is based on a tree which has been pollarded with the nodes at the coarsest resolution exhibiting a grid-based interaction structure. For this class of models, we address the critical problem of parameter estimation. To this end, we derive an EM algorithm on the hybrid structure which mixes an exact EM algorithm on each subtrees and a low cost Gibbsian EM algorithm on the coarse spatial grid. Experiments on a synthetic image and on multi-spectral satellite images are reported.