Feature extraction for butterfly classification using image processing
Loading...
Date
2017-06
Authors
Yap, Jin Hong
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Study on the classification of butterfly species has become very vital to aids entomologist in tracking the habitats and to understand them. Currently, detection and localization of butterflies are done manually by entomologist which skilful and experience enough are capable in recognizing each species by their morphological characteristics of the butterfly. However, this catch-and-release method requires countless of time and patient as well as knowledge to accurately identifies the butterfly. Hence, there is a need for an intelligent system that can automatically identify the butterfly species and to assist entomologist in their research in the future without having the risks of harming the butterfly during the catch-and-release practise. Hence, this proposed project aims to develop a feature extraction based butterfly classification system that applying image processing which is expected to recognize butterfly species accurately according to the processed image. Image captured using electronic devices and cameras are then pre-processed before it is ready for further identification. As mentioned, the identification processes carried out consists of three parts which is the image segmentation, feature extraction and identification. Three types of texture feature extraction, the Law’s Texture Features (LTF), Gabor Filters (GF) and Grey-Level Co-occurrence Matrix (GLCM) are compared in this study to distinguish which methods are the most applicable to be used as feature extraction technique for this system. The classifier employed in this study is K-Nearest Neighbours (kNN) algorithm. 5-fold and 10-fold Cross Validation was carried to determine the value of K in kNN and evaluated the proposed texture analysis methods. The Databases are gathered from Entopia, Penang Butterfly Farm and through sources from the internet. The proposed butterfly species classification system has successfully detected the butterfly species. More samples of butterfly species can be collected via this method compare to conventional approach. Thus, the field work can be done more efficiently and less time consuming.