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.
The Netherlands Organisation for Scientific Research (NWO) is gratefully acknowledged for financial support.
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Balmachnova, E., Florack, L., ter Haar Romeny, B. (2007). Feature Vector Similarity Based on Local Structure. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_33
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DOI: https://doi.org/10.1007/978-3-540-72823-8_33
Publisher Name: Springer, Berlin, Heidelberg
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