GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference

Abstract : This paper presents GraphBPT, a tool for hierarchical representation of images based on binary partition trees. It relies on a new BPT construction algorithm that have interesting tuning properties. Besides, access to image pixels from the tree is achieved efficiently with data compression techniques, and a textual representation of BPT is also provided for interoperability. Finally, we illustrate how the proposed tool takes benefit from probabilistic inference techniques by empowering the BPT with its equivalent factor graph. The relevance of GraphBPT is illustrated in the context of image segmentation.
Type de document :
Communication dans un congrès
International Symposium on Mathematical Morphology, 2015, Reykjavik, Iceland. Springer, 9082, pp.301-312, Lecture Notes in Computer Sciences. <10.1007/978-3-319-18720-4_26>
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https://hal.archives-ouvertes.fr/hal-01168116
Contributeur : Sébastien Lefèvre <>
Soumis le : jeudi 25 juin 2015 - 12:00:51
Dernière modification le : vendredi 17 février 2017 - 16:11:17

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Abdullah Al-Dujaili, François Merciol, Sébastien Lefèvre. GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference. International Symposium on Mathematical Morphology, 2015, Reykjavik, Iceland. Springer, 9082, pp.301-312, Lecture Notes in Computer Sciences. <10.1007/978-3-319-18720-4_26>. <hal-01168116>

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