Compact Vectors of Locally Aggregated Tensors for 3D Shape Retrieval

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dc.contributor.authorTabia, Hedien_US
dc.contributor.authorPicard, Daviden_US
dc.contributor.authorLaga, Hamiden_US
dc.contributor.authorGosselin, Philippe-Henrien_US
dc.contributor.editorUmberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkampen_US
dc.date.accessioned2013-09-24T12:04:05Z
dc.date.available2013-09-24T12:04:05Z
dc.date.issued2013en_US
dc.description.abstractDuring the last decade, a significant attention has been paid, by the computer vision and the computer graphics communities, to three dimensional (3D) object retrieval. Shape retrieval methods can be divided into three main steps: the shape descriptors extraction, the shape signatures and their associated similarity measures, and the machine learning relevance functions. While the first and the last points have vastly been addressed in recent years, in this paper, we focus on the second point; presenting a new 3D object retrieval method using a new coding/pooling technique and powerful 3D shape descriptors extracted from 2D views. For a given 3D shape, the approach extracts a very large and dense set of local descriptors. From these descriptors, we build a new shape signature by aggregating tensor products of visual descriptors. The similarity between 3D models can then be efficiently computed with a simple dot product. We further improve the compactness and discrimination power of the descriptor using local Principal Component Analysis on each cluster of descriptors. Experiments on the SHREC 2012 and the McGill benchmarks show that our approach outperforms the state-of-the-art techniques, including other BoF methods, both in compactness of the representation and in the retrieval performance.en_US
dc.description.sectionheadersFull Papersen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-44-6en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR13/017-024en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.3.3 [ Information storage and retrieval]en_US
dc.subjectInformation Search and Retrievalen_US
dc.subjectI.5.4 [Pattern recognition]en_US
dc.subjectApplicationsen_US
dc.titleCompact Vectors of Locally Aggregated Tensors for 3D Shape Retrievalen_US
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