Feature vector similarity based on local structure

Authors:
Evgeniya Balmachnova;Luc Florack;Bart Ter Haar Romeny
Affiliations:
Eindhoven University of Technology, Eindhoven, The Netherlands;Eindhoven University of Technology, Eindhoven, The Netherlands;Eindhoven University of Technology, Eindhoven, The Netherlands
Venue:
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Year:
2007

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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.