Abstract : In high-level scene interpretation, it is useful to exploit the evolving probabilistic context for stepwise interpretation decisions. We present a new approach based on a general probabilistic framework and beam search for exploring alternative interpretations. As probabilistic scene models, we propose Bayesian Compositional Hierarchies (BCHs) which provide object-centered representations of compositional hierarchies and efficient evidence-based updates. It is shown that a BCH can be used to represent the evolving context during stepwise scene interpretation and can be combined with low-level image analysis to provide dynamic priors for object classification, improving classification and interpretation. Experimental results are presented illustrating the feasibility of the approach for the interpretation of facade images.
Max Bramer. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-331, pp.155-164, 2010, Artificial Intelligence in Theory and Practice III. 〈10.1007/978-3-642-15286-3_15〉
https://hal.inria.fr/hal-01054597
Contributeur : Hal Ifip
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Soumis le : jeudi 7 août 2014 - 15:13:06
Dernière modification le : vendredi 11 août 2017 - 11:17:31
Document(s) archivé(s) le : mercredi 26 novembre 2014 - 01:50:33
Bernd Neumann, Kasim Terzic. Context-Based Probabilistic Scene Interpretation. Max Bramer. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-331, pp.155-164, 2010, Artificial Intelligence in Theory and Practice III. 〈10.1007/978-3-642-15286-3_15〉. 〈hal-01054597〉
