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Mathematical Problems in Engineering
Volume 2014, Article ID 986271, 12 pages
Research Article

Image Processing Method for Automatic Discrimination of Hoverfly Species

1Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
2Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, Povo, 38123 Trentino, Italy
3Department of Biology and Ecology, University of Novi Sad, Trg Dositeja Obradovića 2, 21000 Novi Sad, Serbia

Received 27 June 2014; Accepted 17 December 2014; Published 30 December 2014

Academic Editor: Andrzej Swierniak

Copyright © 2014 Vladimir Crnojević et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy.