NDLI: Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion

Content Provider	IEEE Xplore Digital Library
Author	Habib, T. Inglada, J. Mercier, G. Chanussot, J.
Copyright Year	2008
Description	Author affiliation: Centre Nat. d'Etudes Spatiales, Toulouse (Inglada, J.) \|\| Dept. ITI, Telecom Bretagne (Mercier, G.) \|\| Signal & Images Dept., GIPSA-Lab. (Habib, T.; Chanussot, J.)
Abstract	Due to their flexibility, and capacity to handle high dimensional vectorial data, support vector machines (SVMs) have become the reference for remote sensing imagery classification. However when processing large amounts of data the SVM classification could be a time consuming process. In this paper a new decomposition scheme of the SVM decision function is proposed. The decomposition is based on using the Taylor series expansion to approximate the kernel function. Then, using the results of the optimization problem of the SVM after the learning phase, this expansion is used to obtain an approximate decision function that provides a trade-off between the classification accuracy and the processing time. This speeds-up the SVM classification if limited processing time is available and favors accuracy if sufficient processing time is available.
Starting Page	865
Ending Page	868
File Size	113748
Page Count	4
File Format	PDF
ISBN	9781424417650
ISSN	15224880
DOI	10.1109/ICIP.2008.4711892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Image classification Kernel Remote sensing Taylor series Function approximation Image processing Application software Humans Taylor series expansion kernel decomposition decision function approximation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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