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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Arbuckle, T., Schröder, S., Steinhage, V., Wittmann, D., 2001. Biodiversity Informatics in Action: Identification and Monitoring of Bee Species Using ABIS Sustainability in the Information Society. 15th Int. Symp. Informatics for Environmental Protection, p.425–430.
Batra, S.W.T., 1988. Automatic Image Analysis for Rapid Identification of Africanized Honey Bees. In: Needham, G.H., Page, R.E., Delfinado-Baker, M., et al. (Eds.), Africanized Honey Bees and Bee Mites. Ellis Horwood, Chichester, UK, p.260–263.
Comaniciu, D., Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603–619. [doi:10.1109/34.1000236]
Gaston, K.J., O’Neill, M.A., 2004. Automated species identification: why not? Philos. Trans. R. Soc. B: Biol. Sci., 359(1444):655–667. [doi:10.1098/rstb.2003.1442]
Kingsbury, N., 2001. Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmon. Anal., 10(3):234–253. [doi:10.1006/acha.2000.0343]
Kokare, M., Biswas, P.K., Chatterji, B.N., 2005. Texture image retrieval using new rotated complex wavelet filters. IEEE Trans. Syst. Man Cybern.-Part B, 35(6):1168–1178. [doi:10.1109/TSMCB.2005.850176]
MacLeod, N., 2007. Automated Taxon Identification in Systematics: Theory, Approaches and Applications. CRC Press, London. [doi:10.1201/9781420008074]
Mayo, M., Watson, A.T., 2007. Automatic species identification of live moths. Knowl.-Based Syst., 20(2):195–202. [doi:10.1016/j.knosys.2006.11.012]
Roth, V., Pogoda, A., Steinhage, V., Schroder, S., 1999. Pattern Recognition Combining Feature- and Pixel-Based Classification within a Real World Application. DAGM Symp. for Pattern Recognition, 21:120–129.
Russell, K.N., Do, M.T., Huff, J.C., Platnick, N.I., 2007. Introducing SPIDA-Web: Wavelets, Neural Networks and Internet Accessibility in an Image-Based Automated Identification System. In: MacLeod, N. (Ed.), Automated Object Identification in Systematics: Theory, Approaches, and Applications. CRC Press, Boca Raton, p.131–152.
Schroder, S., Wittmann, D., Drescher, W., Roth, V., Steinhage, V., Cremers, A.B., 2002. The New Key to Bees: Automated Identification by Image Analysis of Wings. Proc. Workshop on Conservation and Sustainable Use of Pollinators in Agriculture, p.209–218.
Shi, J., Malik, J., 2000. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888–905. [doi:10.1109/34.868688]
Tofilski, A., 2004. DrawWing, a program for numerical description of insect wings. J. Insect Sci., 4, Article 17, p.1–5.
Wang, J.Z., Li, J., Gray, R.M., Wiederhold, G., 2001a. Unsupervised multiresolution segmentation for images with low depth of field. IEEE Trans. Pattern Anal. Mach. Intell., 23(1):85–91. [doi:10.1109/34.899949]
Wang, J.Z., Li, J., Wiederhold, G., 2001b. SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LI-braries. IEEE Trans. Pattern Anal. Mach. Intell., 23(9): 947–963. [doi:10.1109/34.955109]
Watson, A.T., O’Neill, M.A., Kitching, I.J., 2003. Automated identification of live moths (Macrolepidoptera) using the Digital Automated Identification SYstem (DAISY). System. Biodivers., 1(3):287–300. [doi:10.1017/S1477200003001208]
Weeks, P.J.D., O’Neill, M.A., Gaston, K.J., Gauld, I.D., 1999. Automating insect identification: exploring the limitations of a prototype system. J. Appl. Entomol., 123(1):1–8. [doi:10.1046/j.1439-0418.1999.00307.x]
Yu, D.S., Kokko, E.G., Barron, J.R., Schaalje, G.B., Gowen, B.E., 1992. Identification of ichneumonid wasps using image analysis of wings. System. Entomol., 17(4):389–395. [doi:10.1111/j.1365-3113.1992.tb00558.x]
Zhu, L.Q., Zhang, Z., 2010. Auto-Classification of Insect Images Based on Color Histogram and GLCM. 7th Int. Conf. on Fuzzy Systems and Knowledge Discovery, 6:2589–2593. [doi:10.1109/FSKD.2010.5569848]
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Project (No. 2006AA10Z211) supported by the National High-Tech Research and Development Program (863) of China
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Zhu, Lq., Zhang, Z. Insect recognition based on integrated region matching and dual tree complex wavelet transform. J. Zhejiang Univ. - Sci. C 12, 44–53 (2011). https://doi.org/10.1631/jzus.C0910740
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DOI: https://doi.org/10.1631/jzus.C0910740
Key words
- Lepidopteran insects
- Auto-classification
- k-means algorithm
- Integrated region matching (IRM)
- Dual tree complex wavelet transform (DTCWT)