An Assessment of Alternative Strategies for Constructing EMD-Based Kernel Functions for Use in an SVM for Image Classification
|Title:||An Assessment of Alternative Strategies for Constructing EMD-Based Kernel Functions for Use in an SVM for Image Classification||Authors:||Zamolotskikh, Anton; Cunningham, 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 machines; Machine learning; Kernelizing; Diagonal 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of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|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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