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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

Cited: 2

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


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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%T Insect recognition based on integrated region matching and dual tree complex wavelet transform
%A Le-qing Zhu
%A Zhen Zhang
%J Journal of Zhejiang University SCIENCE C
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%N 1
%P 44-53
%@ 1869-1951
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910740

T1 - Insect recognition based on integrated region matching and dual tree complex wavelet transform
A1 - Le-qing Zhu
A1 - Zhen Zhang
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 1
SP - 44
EP - 53
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910740

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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