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dc.contributor.authorZolghadr, Esfandiar
dc.contributor.authorFurht, Borko
dc.contributor.editorSkala, Václav
dc.identifier.citationWSCG 2016: full papers proceedings: 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, p. 47-54.en
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.description.abstractIn this paper, a new framework for scene understanding using multi-modal high-ordered context-model is introduced. Spatial and semantical interactions are considered as sources of context which are incorporated in the model using a single object-scene relevance measure that quantifies high-order object relations. This score is used to minimize semantical inconsistencies among objects in dense graph representation of the scene category during the object recognition process. New context model is later incorporated in a Conditional Random Fields (CRF) framework to combine contextual cues with appearance descriptors in order to increase object localization and class prediction accuracy. A novel context-based non-central hypergeometric unary potential is defined to maximize the semantical coherence in the scene. Further refinement is performed using context-based pairwise and high-order potentials which use alpha-expansion and graph-cut to find optimal configuration. Comparison between the purposed approach and state-of-art algorithms shows effectiveness of this approach in annotation and interpretation of scenes.en
dc.format8 s.cs
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2016: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectrozpoznání scény založené na kontextucs
dc.subjectkontrolovaná klasifikacecs
dc.subjectgenerativní modelcs
dc.subjectreprezentativní funkcecs
dc.titleScene understanding using context-based conditional random fielden
dc.typekonferenční příspěvekcs
dc.subject.translatedcontext-based scene recognitionen
dc.subject.translatedsupervised classificationen
dc.subject.translatedgenerative modelen
dc.subject.translatedrepresentative featureen
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/29530

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