EVIDENTIAL COMBINATION OF ONTOLOGICAL AND STATISTICAL INFORMATION FOR ACTIVE SCENE CLASSIFICATION
by , ,
Abstract:
We introduce an information-driven scene classification system that combines different types of knowledge derived from a domain ontology and a statistical model in order to analyze scenes based on recognized objects. The domain ontology structures and formalizes which kind of scene classes exist and which object classes occur in them. Based on this structure, an empirical analysis of annotations from the LabelMe image database results in a statistical domain description. Both forms of knowledge are utilized for determining which object class detector to apply to the current scene according to the principle of maximum information gain. All evidence is combined in a belief-based framework that explicitly takes into account the uncertainty inherent to the statistical model and the object detection process as well as the ignorance associated with the coarse granularity of ontological constraints. Finally, we present preliminary classification performance results for scenes from the LabelMe database.
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PDF URL: http://dx.doi.org/10.5220/0002304300720079
Reference:
EVIDENTIAL COMBINATION OF ONTOLOGICAL AND STATISTICAL INFORMATION FOR ACTIVE SCENE CLASSIFICATION (T. Reineking, N. Schult, J. Hois), In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Scitepress, 2009.
Bibtex Entry:
@InProceedings{Reineking2009,
  author    = {T. Reineking and N. Schult and J. Hois},
  title     = {{EVIDENTIAL} {COMBINATION} {OF} {ONTOLOGICAL} {AND} {STATISTICAL} {INFORMATION} {FOR} {ACTIVE} {SCENE} {CLASSIFICATION}},
  booktitle = {Proceedings of the International Conference on Knowledge Engineering and Ontology Development},
  year      = {2009},
  publisher = {Scitepress},
  abstract  = {We introduce an information-driven scene classification system that combines different types of knowledge derived from a domain ontology and a statistical model in order to analyze scenes based on recognized objects. The domain ontology structures and formalizes which kind of scene classes exist and which object classes occur in them. Based on this structure, an empirical analysis of annotations from the LabelMe image database results in a statistical domain description. Both forms of knowledge are utilized for determining which object class detector to apply to the current scene according to the principle of maximum information gain. All evidence is combined in a belief-based framework that explicitly takes into account the uncertainty inherent to the statistical model and the object detection process as well as the ignorance associated with the coarse granularity of ontological constraints. Finally, we present preliminary classification performance results for scenes from the LabelMe database.},
  doi       = {10.5220/0002304300720079},
  url       = {10.5220/0002304300720079">http://dx.doi.org/10.5220/0002304300720079},
}