Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D medical volume segmentation using hybrid multiresolution statistical approaches
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
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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 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 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.