@inproceedings{05b86d904882497baa966af61ca93590,
title = "Feature vector similarity based on local structure",
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.",
author = "Evguenia Balmachnova and Luc Florack and Romeny, \{Bart M. ter Haar\}",
year = "2007",
doi = "10.1007/978-3-540-72823-8\_33",
language = "English",
isbn = "978-3-540-72822-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "386--393",
editor = "F. Sgallari and A. Murli and N. Paragios",
booktitle = "Proceedings of the First International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) 30 May - 2 June 2007, Ischia, Italy",
address = "Germany",
note = "First International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) ; Conference date: 30-05-2007 Through 02-06-2007",
}