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dc.contributor.authorGuerrero, Paul
dc.contributor.authorMitra, Niloy J.
dc.contributor.authorWonka, Peter
dc.date.accessioned2016-10-12T09:15:28Z
dc.date.available2016-10-12T09:15:28Z
dc.date.issued2016-07-11
dc.identifier.citationGuerrero P, Mitra NJ, Wonka P (2016) RAID. ACM Transactions on Graphics 35: 1–12. Available: http://dx.doi.org/10.1145/2897824.2925939.
dc.identifier.issn0730-0301
dc.identifier.doi10.1145/2897824.2925939
dc.identifier.urihttp://hdl.handle.net/10754/620947
dc.description.abstractAs humans, we regularly interpret scenes based on how objects are related, rather than based on the objects themselves. For example, we see a person riding an object X or a plank bridging two objects. Current methods provide limited support to search for content based on such relations. We present RAID, a relation-augmented image descriptor that supports queries based on inter-region relations. The key idea of our descriptor is to encode region-to-region relations as the spatial distribution of point-to-region relationships between two image regions. RAID allows sketch-based retrieval and requires minimal training data, thus making it suited even for querying uncommon relations. We evaluate the proposed descriptor by querying into large image databases and successfully extract nontrivial images demonstrating complex inter-region relations, which are easily missed or erroneously classified by existing methods. We assess the robustness of RAID on multiple datasets even when the region segmentation is computed automatically or very noisy.
dc.description.sponsorshipThe research described here was supported by the Office of Sponsored Research (OSR) under Award No. OCRF-2014-CGR3-62140401, the Visual Computing Center at KAUST, ERC Starting Grant SmartGeometry (StG-2013 335373), Marie Curie CIG 303541 and the Open3D Project (EPSRC Grant EP/M013685/1).
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=2897824.2925939
dc.relation.urlhttps://youtu.be/NijqS1ughu4
dc.rights(c) 2016 Copyright held by the owner/author(s). This work in licensed under Creative Commons Attribution International 4.0 license.
dc.subjectImage descriptors
dc.titleRAID: a relation-augmented image descriptor
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.conference.date2016-07-24 to 2016-07-28
dc.conference.nameACM SIGGRAPH 2016
dc.conference.locationAnaheim, CA, USA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity College London
dc.relation.embedded<iframe width="560" height="315" src="https://www.youtube.com/embed/NijqS1ughu4?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
dc.identifier.arxividarXiv:1510.01113
kaust.personGuerrero, Paul
kaust.personWonka, Peter
kaust.grant.numberOCRF-2014-CGR3-62140401
refterms.dateFOA2018-06-14T08:23:14Z


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