Automatic classification of insects using color-based and shape-based descriptors

• Main Authors: Hassan, Siti Noorul Asiah, Abdul Rahman, Nur Nadiah Syakira, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
• Format: Article
• Language: English
• Published: Wireilla Scientific Publications, Australia 2014
• Subjects: Q Science (General)
• Online Access: http://irep.iium.edu.my/38133/1/2214ijaceee03.pdf; http://irep.iium.edu.my/38133/; http://wireilla.com/engg/ijaceee/papers/2214ijaceee03.pdf

Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account for more than two thirds of these known species. Since 400 million years ago, there have been various kinds of interactions between humans and insects. There have been several attempts to create a method to perform insect identification accurately. Great knowledge and experience on entomology are required for accurate insect identification. Automation of insect identification is required because there is a shortage of skilled entomologists. We propose an automatic insect identification framework that can identify grasshoppers and butterflies from colored images. Two classes of insects are chosen for a proof-of-concept. Classification is achieved by manipulating insects’ color and their shape feature since each class of sample case has different color and distinctive body shapes. The proposed insect identification process starts by extracting features from samples and splitting them into two training sets. One training emphasizes on computing RGB features while the other one is normalized to estimate the area of binary color that signifies the shape of the insect. SVM classifier is used to train the data obtained. Final decision of the classifier combines the result of these two features to determine which class an unknown instance belong to. The preliminary results demonstrate the efficacy and efficiency of our two-step automatic insect identification approach and motivate us to extend this framework to identify a variety of other species of insects.