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Authors: Dávid Losteiner 1 ; László Havasi 2 and Tamás Szirányi 3

Affiliations: 1 Péter Pázmány Catholic University, Hungary ; 2 MTA SZTAKI, Hungary ; 3 Computer And Automation Research Institute, Hungarian Academy Of Sciences, Hungary

ISBN: 978-989-8111-69-2

Keyword(s): SIFT, Dimension reduction, DTW, Image descriptors.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Image Registration ; Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: The descriptors used for image indexing - e.g. Scale Invariant Feature Transform (SIFT) - are generally parameterized in very high dimensional spaces which guarantee the invariance on different light conditions, orientation and scale. The number of dimensions limit the performance of search techniques in terms of computational speed. That is why dimension reduction of descriptors is playing an important role in real life applications. In the paper we present a modified version of the most popular algorithm, SIFT. The motivation was to speed up searching on large feature databases in video surveillance systems. Our method is based on the standard SIFT algorithm using a structural property: the local maxima of these high dimensional descriptors. The weighted local positions will be aligned with a dynamic programming algorithm (DTW) and its error is calculated as a new kind of measure between descriptors. In our approach we do not use a training set, pre-computed statistics or any param eters when finding the matches, which is very important for an online video indexing application. (More)

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Paper citation in several formats:
Losteiner D.; Havasi L.; Szirányi T. and (2009). LOCMAX SIFT - Non-Statistical Dimension Reduction on Invariant Descriptors .In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 192-195. DOI: 10.5220/0001799401920195

@conference{visapp09,
author={Dávid Losteiner and László Havasi and Tamás Szirányi},
title={LOCMAX SIFT - Non-Statistical Dimension Reduction on Invariant Descriptors },
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={192-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001799401920195},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - LOCMAX SIFT - Non-Statistical Dimension Reduction on Invariant Descriptors
SN - 978-989-8111-69-2
AU - Losteiner, D.
AU - Havasi, L.
AU - Szirányi, T.
PY - 2009
SP - 192
EP - 195
DO - 10.5220/0001799401920195

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