Article

A boosting approach to content-based 3D model retrieval

Authors:

Masayuki NakajimaAuthors Info & Claims

GRAPHITE '07: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia

Pages 227 - 234

https://doi.org/10.1145/1321261.1321301

Published: 01 December 2007 Publication History

Abstract

We present a new framework for 3D model retrieval based on the assumption that models belonging to the same shape class share the same salient features. The main issue is learning these features. We propose an algorithm for computing these features and their corresponding saliency value. At the learning stage, a large set of features are extracted from every model and a boosting algorithm is applied to learn the classification function in the feature space. AdaBoost learns a classifier that relies on a small subset of the features with the mean of weak classifiers. Moreover it assigns weights to the selected features, that we interpret as a measure of the feature saliency within the class, providing an efficient way for feature selection and combination. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark show significant improvement in the retrieval performance and computation efficiency. We show also that the proposed framework can be applied to the problem of best view selection.

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Cited By

Chen ZTsai CLin W(2021)Cube of Space Sampling for 3D Model RetrievalApplied Sciences10.3390/app11231114211:23(11142)Online publication date: 24-Nov-2021
https://doi.org/10.3390/app112311142
(2018)References3D Shape Analysis10.1002/9781119405207.refs(303-336)Online publication date: 21-Dec-2018
https://doi.org/10.1002/9781119405207.refs
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
https://dl.acm.org/doi/10.1016/j.cviu.2014.10.006
Show More Cited By

A boosting approach to content-based 3D model retrieval
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image ACM Conferences

GRAPHITE '07: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia

December 2007

335 pages

ISBN:9781595939128

DOI:10.1145/1321261

Conference Chair:
Andrew Rohl
iVEC

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Published: 01 December 2007

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GRAPHITE07: International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia

December 1 - 4, 2007

Perth, Australia

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Overall Acceptance Rate 124 of 241 submissions, 51%

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Cited By

Chen ZTsai CLin W(2021)Cube of Space Sampling for 3D Model RetrievalApplied Sciences10.3390/app11231114211:23(11142)Online publication date: 24-Nov-2021
https://doi.org/10.3390/app112311142
(2018)References3D Shape Analysis10.1002/9781119405207.refs(303-336)Online publication date: 21-Dec-2018
https://doi.org/10.1002/9781119405207.refs
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
https://dl.acm.org/doi/10.1016/j.cviu.2014.10.006
Barra VBiasotti S(2014)3D shape retrieval and classification using multiple kernel learning on extended Reeb graphsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-014-0926-530:11(1247-1259)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1007/s00371-014-0926-5
Atmosukarto IShapiro L(2012)3D object retrieval using salient viewsInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0015-32:2(103-115)Online publication date: 8-Jul-2012
https://doi.org/10.1007/s13735-012-0015-3
Marini SPatané GSpagnuolo MFalcidieno B(2010)Feature selection for enhanced spectral shape comparisonProceedings of the 3rd Eurographics conference on 3D Object Retrieval10.5555/2381147.2381156(31-38)Online publication date: 2-May-2010
https://dl.acm.org/doi/10.5555/2381147.2381156
Ohbuchi R(2010)The Journal of The Institute of Image Information and Television Engineers10.3169/itej.64.96764:7(967-972)Online publication date: 2010
https://doi.org/10.3169/itej.64.967
Li ZWang DLi Boyang Zhong Liangliang (2010)3D model classification using salient features for content representation2010 Sixth International Conference on Natural Computation10.1109/ICNC.2010.5584191(3541-3545)Online publication date: Aug-2010
https://doi.org/10.1109/ICNC.2010.5584191
Atmosukarto IWilamowska KHeike CShapiro L(2010)3D object classification using salient point patterns with application to craniofacial researchPattern Recognition10.1016/j.patcog.2009.11.00443:4(1502-1517)Online publication date: 1-Apr-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.11.004
Zhu KWong YLu WFuh J(2009)A diffusion wavelet approach for 3-D model matchingComputer-Aided Design10.1016/j.cad.2008.11.00741:1(28-36)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.1016/j.cad.2008.11.007

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