Article

A comparison framework for 3d object classification methods

Authors:

B. FalcidienoAuthors Info & Claims

MRCS'06: Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security

Pages 314 - 321

https://doi.org/10.1007/11848035_42

Published: 11 September 2006 Publication History

Publisher Site

Abstract

3D shape classification plays an important role in the process of organizing and retrieving models in large databases. Classifying shapes means to assign a query model to the most appropriate class of objects: knowledge about the membership of models to classes can be very useful to speed up and improve the shape retrieval process, by allowing the reduction of the candidate models to compare with the query.

The main contribution of this paper is the setting of a framework to compare the effectiveness of different query-to-class membership measures, defined independently of specific shape descriptors. The classification performances are evaluated against a set of popular 3D shape descriptors, using a dataset consisting of 14 classes made up of 20 objects each.

References

[1]

Tangelder, J., Veltkamp, R.: A survey of content based 3d shape retrieval methods. In: Proc. Shape Modeling Applications 2004. (2004) 145-156.

Digital Library

Google Scholar

[2]

Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranic, D.V.: Feature-based similarity search in 3D object databases. ACM Computing Surveys 37(4) (2005) 345-387.

Digital Library

Google Scholar

[3]

Sengupta, K., Boyer, K.L.: Organizing large structural mordelbases. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(4) (1995) 321-332.

Digital Library

Google Scholar

[4]

Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons Inc. (2001).

Digital Library

Google Scholar

[5]

Lam, W., Keung, C.K., Liu, D.: Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8) (2002) 1075-1090.

Digital Library

Google Scholar

[6]

Donamukkala, R., Huber, D., Kapuria, A., Hebert, M.: Automatic class selection and prototyping of 3-D object classification. In: Proc. 5th Int. Conf. on 3-D Digital Imaging and Modeling /3DIM'05), IEEE (2005) 64-71.

Digital Library

Google Scholar

[7]

Csákáky, P., Wallace, A.M.: Representation and classification of 3-D objects. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybern. 33(4) (2003) 638-647.

Digital Library

Google Scholar

[8]

Huber, D., Kapuria, A., Donamukkala, R., Hebert, M.: Part-based 3D object classification. In: Proc. IEEE Conf. on Computer Vision and pattern Recognition (CVPR'04). Volume 2. (2004) 82-89.

Digital Library

Google Scholar

[9]

Zhang, J.: Selecting typical instances in instance-based learning. In: Proc. Int. conf. Machine Learning. (1992) 470-479.

Digital Library

Google Scholar

[10]

Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In Kobbelt, L., Schröder, P., Hoppe, H., eds.: Proc. Symposium in Geometry Processing. (2003) 156-165.

Digital Library

Google Scholar

[11]

Chen, D., Ouhyoung, M., Tian, X., Shen, Y.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22 (2003) 223-232.

Crossref

Google Scholar

[12]

Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Computer graphics proceedings, annual conference series: SIGGRAPH conference. (2001) 203-212.

Digital Library

Google Scholar

[13]

Biasotti, S., Marini, S.: 3D object comparison based on shape descriptors. International Journal of Computer Applications in Technology 23(2/3/4) (2005) 57-69.

Google Scholar

Cited By

View all

Hamad MThomassey SBruniaux P(2017)A new sizing system based on 3D shape descriptor for morphology clusteringComputers and Industrial Engineering10.1016/j.cie.2017.05.030113:C(683-692)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.cie.2017.05.030
Biasotti SCerri ABronstein ABronstein M(2016)Recent Trends, Applications, and Perspectives in 3D Shape Similarity AssessmentComputer Graphics Forum10.1111/cgf.1273435:6(87-119)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1111/cgf.12734
Tabia HDaoudi MVandeborre JColot O(2013)A parts-based approach for automatic 3D shape categorization using belief functionsACM Transactions on Intelligent Systems and Technology10.1145/2438653.24386684:2(1-16)Online publication date: 3-Apr-2013
https://dl.acm.org/doi/10.1145/2438653.2438668
Show More Cited By

Index Terms

A comparison framework for 3d object classification methods
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Parts-based 3D object classification
CVPR'04: Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

This paper presents a parts-based method for classifying scenes of 3D objects into a set of pre-determined object classes. Working at the part level, as opposed to the whole object level, enables a more flexible class representation and allows scenes in ...
Unify 3D Shape Retrieval and Classification in One Framework
DMCIT '17: Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology

In conventional 3D shape retrieval and classification, they both need to compute the similarity between the input shapes and candidate or training 3D shapes. As for 3D shape classification, it essentially computes the distance between the query shapes ...
A Framework towards the Unification of Ensemble Classification Methods
ICMLA '13: Proceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 02

Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of the improved classification accuracy in different applications. A large variety of ensemble methods have been proposed in order to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MRCS'06: Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security

September 2006

804 pages

ISBN:3540393927

Editors:
Bilge Gunsel
Multimedia Signal Processing and Pattern Recognition Lab., Dept. of Electronics and Communications Eng., Istanbul Technical University, Istanbul, Turkey
,
Anil K. Jain
Department of Computer Science and Engineering, Michigan State University, Istanbul
,
A. Murat Tekalp
Department of Computer Science and Engineering, College of Engineering, Koç University, Sarıyer, İstanbul, Turkey
,
Bülent Sankur
Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Turkey

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 September 2006

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hamad MThomassey SBruniaux P(2017)A new sizing system based on 3D shape descriptor for morphology clusteringComputers and Industrial Engineering10.1016/j.cie.2017.05.030113:C(683-692)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.cie.2017.05.030
Biasotti SCerri ABronstein ABronstein M(2016)Recent Trends, Applications, and Perspectives in 3D Shape Similarity AssessmentComputer Graphics Forum10.1111/cgf.1273435:6(87-119)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1111/cgf.12734
Tabia HDaoudi MVandeborre JColot O(2013)A parts-based approach for automatic 3D shape categorization using belief functionsACM Transactions on Intelligent Systems and Technology10.1145/2438653.24386684:2(1-16)Online publication date: 3-Apr-2013
https://dl.acm.org/doi/10.1145/2438653.2438668
Napoléon TSahbi H(2010)From 2D silhouettes to 3D object retrievalJournal on Image and Video Processing10.1155/2010/3671812010(1-22)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1155/2010/367181
Biasotti SGiorgi DSpagnuolo MFalcidieno B(2008)Reeb graphs for shape analysis and applicationsTheoretical Computer Science10.1016/j.tcs.2007.10.018392:1-3(5-22)Online publication date: 20-Feb-2008
https://dl.acm.org/doi/10.1016/j.tcs.2007.10.018
Atmosukarto IShapiro L(2008)A Learning Approach to 3D Object Representation for ClassificationProceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition10.1007/978-3-540-89689-0_31(267-276)Online publication date: 4-Dec-2008
https://dl.acm.org/doi/10.1007/978-3-540-89689-0_31

Abstract

References

Cited By

Index Terms

Recommendations

Parts-based 3D object classification

Unify 3D Shape Retrieval and Classification in One Framework

A Framework towards the Unification of Ensemble Classification Methods

Comments

Information

Published In

Sponsors

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations