Feature vector similarity based on local structure

Evguenia Balmachnova, Luc Florack, Bart M. ter Haar Romeny

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
84 Downloads (Pure)

Abstract

Local feature matching is an essential component of many image retrieval algorithms. Euclidean and Mahalanobis distances are mostly used in order to compare two feature vectors. The first distance does not give satisfactory results in many cases and is inappropriate in the typical case where the components of the feature vector are incommensurable, whereas the second one requires training data. In this paper a stability based similarity measure (SBSM) is introduced for feature vectors that are composed of arbitrary algebraic combinations of image derivatives. Feature matching based on SBSM is shown to outperform algorithms based on Euclidean and Mahalanobis distances, and does not require any training.
Original languageEnglish
Title of host publicationProceedings of the First International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) 30 May - 2 June 2007, Ischia, Italy
EditorsF. Sgallari, A. Murli, N. Paragios
Place of PublicationBerlin
PublisherSpringer
Pages386-393
Number of pages8
ISBN (Print)978-3-540-72822-1
DOIs
Publication statusPublished - 2007
EventFirst International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) - Ischia, Italy
Duration: 30 May 20072 Jun 2007

Publication series

NameLecture Notes in Computer Science
Volume4485
ISSN (Print)0302-9743

Conference

ConferenceFirst International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007)
Country/TerritoryItaly
CityIschia
Period30/05/072/06/07

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