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

Le-qing ZHU; Zhen ZHANG

doi:10.1631/jzus.C0910740

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

计算机与自动化 | Updated：2022-08-31

- Insect recognition based on integrated region matching and dual tree complex wavelet transform
- Insect recognition based on integrated region matching and dual tree complex wavelet transform
- 信息与电子工程前沿（英文） 2011年12卷第1期页码：44-53
- Affiliations：
  
  1.College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
  2.Key Lab of Forest Protection of State Forestry Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/jzus.C0910740
  中图分类号：
- 收稿：2009-12-04，
  
  修回：2010-11-04，
  
  录用：2010-03-31，
  
  纸质出版：2011-01
- Accepted：
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Insect recognition based on integrated region matching and dual tree complex wavelet transform[J]. 信息与电子工程前沿（英文）, 2011,12(1):44-53.

Le-qing ZHU, Zhen ZHANG. Insect recognition based on integrated region matching and dual tree complex wavelet transform[J]. Frontiers of Information Technology & Electronic Engineering, 2011, 12(1): 44-53.
Insect recognition based on integrated region matching and dual tree complex wavelet transform[J]. 信息与电子工程前沿（英文）, 2011,12(1):44-53. DOI： 10.1631/jzus.C0910740.

Le-qing ZHU, Zhen ZHANG. Insect recognition based on integrated region matching and dual tree complex wavelet transform[J]. Frontiers of Information Technology & Electronic Engineering, 2011, 12(1): 44-53. DOI： 10.1631/jzus.C0910740.

摘要

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

-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

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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