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dc.contributor.authorBarra, Vincenten_US
dc.contributor.authorBiasotti, Silviaen_US
dc.contributor.editorUmberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkampen_US
dc.date.accessioned2013-09-24T12:04:06Z
dc.date.available2013-09-24T12:04:06Z
dc.date.issued2013en_US
dc.identifier.isbn978-3-905674-44-6en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttp://dx.doi.org/10.2312/3DOR/3DOR13/025-032en_US
dc.description.abstractThis paper addresses 3D shape classification and retrieval in terms of supervised selection of the most significant features in a space of attributed graphs encoding different shape characteristics. For this purpose, 3D models are represented as bags of shortest paths defined over well chosen Extended Reeb graphs, while the similarity between pairs of Extended Reeb graphs is addressed through kernels adapted to these descriptions. Given this set of kernels, a Multiple Kernel Learning algorithm is used to find an optimal linear combination of kernels for classification and retrieval purposes. Results are comparable with the best results of the literature, and the modularity and flexibility of the kernel learning ensure its applicability to a large set of methods.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer Graphics [I.3.6]en_US
dc.subjectMethodology and Techniquesen_US
dc.subjectInformation storage and retrieval [H.3.3]en_US
dc.subjectInformation search and Retrievalen_US
dc.titleLearning Kernels on Extended Reeb Graphs for 3D Shape Classification and Retrievalen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.description.sectionheadersFull Papersen_US


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