Title: Scene understanding using context-based conditional random field
Authors: Zolghadr, Esfandiar
Furht, Borko
Citation: WSCG 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.
Issue Date: 2016
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2016/!!_CSRN-2601.pdf
ISBN: 978-80-86943-57-2
ISSN: 2464–4617 (print)
2464–4625 (CD-ROM)
Keywords: rozpoznání scény založené na kontextu;kontrolovaná klasifikace;generativní model;reprezentativní funkce
Keywords in different language: context-based scene recognition;supervised classification;generative model;representative feature
Abstract: In 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.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2016: Full Papers Proceedings

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