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
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 para
meters when finding the matches, which is very important for an online video indexing application.
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