A Computer Vision System for the Automatic Identification of Butterfly Species via Gabor-Filter-Based Texture Features and Extreme Learning Machine: GF+ELM

Abstract

Butterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a new method based on the extreme learning machine (ELM) and Gabor filters (GFs), which is an image processing technique, was used for the identification of butterfly species as an alternative to conventional diagnostic methods. GFs have been recognized as a very useful tool in texture analysis, due to their optimal localization properties in both the spatial and frequency domains, and have been found to have widespread use in computer vision applications. To obtain the appropriate features from butterfly images in the spatial domain, 20 filters were designed for the various angles and frequencies (5 frequencies and 4 orientations). The diagnosing of butterflies was performed through ELM, with texture features based on GFs. The classification process was performed with a 75%–25% training-test set for different activation functions and the recognition performance value was obtained as 97.00%. In addition, the recognition success rates with ELM were compared to other machine learning methods and it was seen that ELM has a more significant success rate in butterfly identification than other methods. As a result, the proposed method is a suitable machine vision system for detecting butterfly species