An Assessment of Alternative Strategies for Constructing EMD-Based Kernel Functions for Use in an SVM for Image Classification

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Title: An Assessment of Alternative Strategies for Constructing EMD-Based Kernel Functions for Use in an SVM for Image Classification
Authors: Zamolotskikh, AntonCunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/12359
Date: 21-Mar-2007
Online since: 2021-07-29T16:10:33Z
Abstract: Because of their sound theoretical underpinnings, Support Vector Machines (SVMs) have very impressive performance in classification. However, the use of SVMs is constrained by the fact that the affinity measure that is used to build the classifier must produce a kernel matrix that is positive semi-definite (PSD). This is normally not a problem, however many very effective affinity measures are known that will not produce a PSD kernel matrix. One such measure is the EarthMover’s Distance (EMD) for quantifying the difference between images. In this paper we consider three methods for producing a PSD kernel from the EMD and compare SVM-based classifiers that use these measures against a Nearest Neighbour classifier built directly on the EMD. We find that two of these kernelised EMD measures are effective and the resulting SVMs are better than the Nearest Neighbour alternatives.
Type of material: Technical Report
Publisher: University College Dublin. School of Computer Science and Informatics
Series/Report no.: UCD CSI Technical Reports; UCD-CSI-2007-2
Copyright (published version): 2007 the Authors
Keywords: Support vector machinesMachine learningKernelizingDiagonal shift
Other versions: https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html
Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science and Informatics Technical Reports

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