Options
An Assessment of Alternative Strategies for Constructing EMD-Based Kernel Functions for Use in an SVM for Image Classification
Author(s)
Date Issued
2007-03-21
Date Available
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
UCD CSI Technical Reports
UCD-CSI-2007-2
Copyright (Published Version)
2007 the Authors
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
File(s)
Owning collection
Views
277
Acquisition Date
Mar 25, 2024
Mar 25, 2024
Downloads
131
Last Week
1
1
Last Month
5
5
Acquisition Date
Mar 25, 2024
Mar 25, 2024