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CLC number: TP391.41

On-line Access: 2010-01-10

Received: 2009-12-04

Revision Accepted: 2010-03-31

Crosschecked: 2010-11-04

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.1 P.44-53

http://doi.org/10.1631/jzus.C0910740


Insect recognition based on integrated region matching and dual tree complex wavelet transform


Author(s):  Le-qing Zhu, Zhen Zhang

Affiliation(s):  College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China, Key Lab of Forest Protection of State Forestry Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China

Corresponding email(s):   zhangzhen@caf.ac.cn

Key Words:  Lepidopteran insects, Auto-classification, k-means algorithm, Integrated region matching (IRM), Dual tree complex wavelet transform (DTCWT)


Le-qing Zhu, Zhen Zhang. Insect recognition based on integrated region matching and dual tree complex wavelet transform[J]. Journal of Zhejiang University Science C, 2011, 12(1): 44-53.

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%A Zhen Zhang
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%I Zhejiang University Press & Springer
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T1 - Insect recognition based on integrated region matching and dual tree complex wavelet transform
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910740


Abstract: 
To provide pest technicians with a convenient way to recognize insects, a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT). The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated. The ROI is first segmented with the k-means algorithm into regions according to the color features, properties of all the segmented regions being used as a coarse level feature. The color image is then converted to a grayscale image, where DTCWT features are extracted as a fine level feature. The IRM scheme is undertaken to find K nearest neighbors (KNNs), out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features. The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%. For the forewing subset, a recognition accuracy of 92.38% was achieved. The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

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