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dc.contributor.authorYang, Yifan
dc.contributor.authorAtif, Jamal
dc.contributor.authorBloch, Isabelle
dc.date.accessioned2017-02-10T14:34:22Z
dc.date.available2017-02-10T14:34:22Z
dc.date.issued2015
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16248
dc.language.isoenen
dc.subjectKR
dc.subjectreasoning
dc.subjectabduction
dc.subject.ddc006.3en
dc.titleAbductive reasoning using tableau methods for high-level image interpretation
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherTélécom-ParisTech
dc.description.abstractenImage interpretation is a dynamic research domain involving not only the detection of objects in a scene but also the semantic description considering context information in the whole scene. Image interpretation problem can be formalized as an abductive reasoning problem, i.e. an inference to the best explanation using a background knowledge. In this work, we present a framework using a tableau method for generating and selecting potential explanations of the given image when the background knowledge is encoded using a description that is able to handle spatial relations.
dc.identifier.citationpages356-365
dc.relation.ispartoftitleKI 2015: Advances in Artificial Intelligence: 38th Annual German Conference on AI
dc.relation.ispartofeditorSteffen Hölldobler, Markus Krötzsch, Rafael Peñaloza, Sebastian Rudolph
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofdate2015
dc.relation.ispartofurl10.1007/978-3-319-24489-1
dc.contributor.countryeditoruniversityotherFRANCE
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-319-24489-1
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-319-24489-1_34
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-02-10T14:42:35Z
hal.person.labIds162010
hal.person.labIds989
hal.person.labIds162010
hal.identifierhal-01464864*


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