Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval

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dc.contributor.authorAkgül, Ceyhun Buraken_US
dc.contributor.authorSankur, Bülenten_US
dc.contributor.authorYemez, Yücelen_US
dc.contributor.authorSchmitt, Francisen_US
dc.contributor.editorStavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmannen_US
dc.date.accessioned2013-10-21T18:15:19Z
dc.date.available2013-10-21T18:15:19Z
dc.date.issued2008en_US
dc.description.abstractIn this work, we introduce a score fusion scheme to improve the 3D object retrieval performance. The state of the art in 3D object retrieval shows that no single descriptor is capable of providing fine grain discrimination required by prospective 3D search engines. The proposed fusion algorithm linearly combines similarity information originating from multiple shape descriptors and learns their optimal combination of weights by minimizing the empirical ranking risk criterion. The algorithm is based on the statistical ranking framework [CLV07], for which consistency and fast rate of convergence of empirical ranking risk minimizers have been established. We report the results of ontology-driven and relevance feedback searches on a large 3D object database, the Princeton Shape Benchmark. Experiments show that, under query formulations with user intervention, the proposed score fusion scheme boosts the performance of the 3D retrieval machine significantly.en_US
dc.description.seriesinformationEurographics 2008 Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-05-7en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR08/041-048en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): H.3.3 [Information Search and Retrieval]: Retrieval Models I.5.1 [Models]: Statisticalen_US
dc.titleSimilarity Score Fusion by Ranking Risk Minimization for 3D Object Retrievalen_US
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