A new automatic identification system of insect images at the order level

Authors:
Jiangning Wang;Congtian Lin;Liqiang Ji;Aiping Liang
Affiliations:
Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Beijing 100101, China and Key Laboratory of Zoological Systematic ...;Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Beijing 100101, China;Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Beijing 100101, China;Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Beijing 100101, China
Venue:
Knowledge-Based Systems
Year:
2012

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Abstract

A new automatic identification system has been designed to identify insect specimen images at the order level. Several relative features were designed according to the methods of digital image progressing, pattern recognition and the theory of taxonomy. Artificial neural networks (ANNs) and a support vector machine (SVM) are used as pattern recognition methods for the identification tests. During tests on nine common orders and sub-orders with an artificial neural network, the system performed with good stability and accuracy reached 93%. Results from tests using the support vector machine further improved accuracy. We also did tests on eight- and nine-orders with different features and based on these results we compare the advantages and disadvantages of our system and provide some advice for future research on insect image recognition.