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There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding
Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
Cognitive Artificial Intelligence, Radboud University Nijmegen, Nijmegen, The Netherlands.
Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
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2014 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 123, p. 75-85Article in journal (Refereed) Published
Abstract [en]

Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction, qualitative spatial relations, relational instance-based learning and compositional hierarchies in one framework. For each layer in the hierarchy, qualitative spatial structures in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and subsequently detect corners, windows, doors, and individual houses.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 123, p. 75-85
Keywords [en]
Relational representations, Relational instance-based learning, Hierarchical image understanding
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-86417DOI: 10.1016/j.neucom.2012.10.037ISI: 000326909600009Scopus ID: 2-s2.0-84885866109OAI: oai:DiVA.org:oru-86417DiVA, id: diva2:1475652
Available from: 2020-10-13 Created: 2020-10-13 Last updated: 2020-11-16Bibliographically approved

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De Raedt, Luc

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