Article

Learning semantic categories for 3D model retrieval

Authors:

Ryutarou Ohbuchi,

Akihiro Yamamoto,

Jun KobayashiAuthors Info & Claims

MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval

Pages 31 - 40

https://doi.org/10.1145/1290082.1290090

Published: 24 September 2007 Publication History

Abstract

A shape similarity judgment among a pair of 3D models is often influenced by their semantics, in addition to their shapes. If we could somehow incorporate semantic knowledge into a "shape similarity" comparison method, retrieval performance of a shape-based 3D model retrieval system could be improved. This paper presents a 3D model retrieval method that successfully incorporates semantic information from human-made categories (labels) in a training database. Our off-line, 2-stage semi-supervised approach learns efficiently from a small set of labeled models. The method first performs unsupervised learning from a large set of unlabeled 3D models to find a non-linear subspace on which the shape features are distributed. It then performs a supervised learning from a much smaller set of labeled 3D models to learn multiple semantic categories at once. Our experimental evaluation showed that the retrieval performance using proposed method is significantly higher than those of both supervised-only and unsupervised-only learning methods.

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Furuya TOhbuchi RMao XThalmann DGavrilova M(2017)Deep semantic hashing of 3D geometric features for efficient 3D model retrievalProceedings of the Computer Graphics International Conference10.1145/3095140.3095148(1-6)Online publication date: 27-Jun-2017
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Learning semantic categories for 3D model retrieval

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Published In

cover image ACM Conferences

MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval

September 2007

343 pages

ISBN:9781595937780

DOI:10.1145/1290082

General Chairs:
James Z. Wang
The Pennsylvania State University, USA
,
Nozha Boujemaa
INRIA Rocquencourt, France
,
Program Chairs:
Alberto Del Bimbo
University of Florence, Italy
,
Jia Li
The Pennsylvania State University, USA

Copyright © 2007 ACM.

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Published: 24 September 2007

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September 24 - 29, 2007

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https://dl.acm.org/doi/10.1007/s10055-018-0333-8
Furuya TOhbuchi RMao XThalmann DGavrilova M(2017)Deep semantic hashing of 3D geometric features for efficient 3D model retrievalProceedings of the Computer Graphics International Conference10.1145/3095140.3095148(1-6)Online publication date: 27-Jun-2017
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Li BLu YJohan HFares R(2017)Sketch-based 3D model retrieval utilizing adaptive view clustering and semantic informationMultimedia Tools and Applications10.1007/s11042-016-4187-376:24(26603-26631)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11042-016-4187-3
Liang HChang JWang MChen CZhang JCai YThalmann D(2016)Semantic framework for interactive animation generationProceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 110.1145/3013971.3013998(137-145)Online publication date: 3-Dec-2016
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Li BLu YLi CGodil ASchreck TAono MBurtscher MChen QChowdhury NFang BFu HFuruya TLi HLiu JJohan HKosaka RKoyanagi HOhbuchi RTatsuma AWan YZhang CZou C(2015)A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queriesComputer Vision and Image Understanding10.1016/j.cviu.2014.10.006131:C(1-27)Online publication date: 1-Feb-2015
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