Classification of SURF Image Features by Selected Machine Learning Algorithms

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU124207" target="_blank" >RIV/00216305:26220/17:PU124207 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8076064" target="_blank" >https://ieeexplore.ieee.org/document/8076064</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2017.8076064" target="_blank" >10.1109/TSP.2017.8076064</a>

Result language
angličtina
Original language name
Classification of SURF Image Features by Selected Machine Learning Algorithms
Original language description
We have proposed a concept for classification interesting points in images by means of a machine learning approach. The basic idea is that each interesting point detected in an image is classified either as a point belonging to some trained model (e.g. corner of a license plate) or not. During the first stage, we detected interesting points in a set of images by the well-known SURF method. Then we have employed supervised learning algorithms LDA, QDA, Naive Bayes, Decision tree and SVM to create relevant models of corners in images. Finally, all generated models were evaluated during classification stage by a cross-validation technique and an example experiment of license plate detection has been carried out and is introduced at the very end of this paper. Interesting outcomes have been obtained by the Naive Bayes algorithm resulting in a sensitivity value of the 100% and an accuracy value of the 99.8% on the real-world gallery of 535 images containing over 93 thousand interesting points. Although our gallery is not vast, the results are really promising to use our concept in another applications of robust and real-time object recognition.
Czech name
—
Czech description
—

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 40th International Conference on Telecommunications and Signal Processing
ISBN
978-1-5090-3981-4
ISSN
1805-5435
e-ISSN
—
Number of pages
6
Pages from-to
636-641
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Barcelona
Event location
Barcelona
Event date
Jul 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000425229000136

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