research-article

Support Vector Machines for 3D Object Recognition

Authors:

Massimiliano Pontil,

Alessandro VerriAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Issue 6

Pages 637 - 646

https://doi.org/10.1109/34.683777

Published: 01 June 1998 Publication History

Abstract

Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7,200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.

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Cited By

Muzahid AHan HZhang YLi DZhang YJamshid JSohel F(2024)Deep learning for 3D object recognitionNeurocomputing10.1016/j.neucom.2024.128436608:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128436
Shrivastava SShukla SKhare N(2024)Support vector machine with eagle loss functionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122168238:PEOnline publication date: 27-Feb-2024
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Index Terms

Support Vector Machines for 3D Object Recognition
1. Computing methodologies

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 20, Issue 6

June 1998

95 pages

ISSN:0162-8828

Editor:
Rangachar Kasturi
Pennsylvania State Univ., Univ. Park

Issue’s Table of Contents

Copyright © Copyright © 1998 IEEE. All Rights Reserved.

Publisher

IEEE Computer Society

United States

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Published: 01 June 1998

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Muzahid AHan HZhang YLi DZhang YJamshid JSohel F(2024)Deep learning for 3D object recognitionNeurocomputing10.1016/j.neucom.2024.128436608:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128436
Shrivastava SShukla SKhare N(2024)Support vector machine with eagle loss functionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122168238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122168
Shrivastava SShukla SKhare N(2023)A stable variant of linex loss SVM for handling noise with reduced hyperparametersInformation Sciences: an International Journal10.1016/j.ins.2023.119402646:COnline publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119402
Ma YWang C(2022)SdcNet for object recognitionComputer Vision and Image Understanding10.1016/j.cviu.2021.103332215:COnline publication date: 1-Jan-2022
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Rani SLakhwani KKumar S(2022)Three dimensional objects recognition & pattern recognition technique; related challenges: A reviewMultimedia Tools and Applications10.1007/s11042-022-12412-281:12(17303-17346)Online publication date: 1-May-2022
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