Automatic Hoverfly Species Discrimination

Abstract

An novel 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 histograms of oriented gradients and local binary patterns features. The features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract simple statistics concerning the distances of these points from the centroid. Such simple features can be used to achieve automatic discrimination of four selected hoverfly species, using a Multi Layer Perceptron (MLP) neural network classifier. The proposed approach achieves classification accuracy of environ 71%.