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      Automatic species identification of live moths

      Mayo, Michael; Watson, Anna T.
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      Automatic species identification of live moths.pdf
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      DOI
       10.1016/j.knosys.2006.11.012
      Link
       www.sciencedirect.com
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      Mayo M. & Watson A.T. (2007). Automatic species identification of live moths. Knowledge-Based Systems, 20(2), 195-202.
      Permanent Research Commons link: https://hdl.handle.net/10289/1389
      Abstract
      A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35 different UK species, was analysed to determine if data mining techniques could be used effectively for automatic species identification. Feature vectors were extracted from each of the moth images and the machine learning toolkit WEKA was used to classify the moths by species using the feature vectors. Whereas a previous analysis of this image dataset reported in the literature [A. Watson, M. O'Neill, I. Kitching, Automated identification of live moths (Macrolepidoptera) using Digital Automated Identification System (DAISY), Systematics and Biodiversity 1 (3) (2004) 287-300.] required that each moth's least worn wing region be highlighted manually for each image, WEKA was able to achieve a greater level of accuracy (85%) using support vector machines without manual specification of a region of interest at all. This paper describes the features that were extracted from the images, and the various experiments using different classifiers and datasets that were performed. The results show that data mining can be usefully applied to the problem of automatic species identification of live specimens in the field.
      Date
      2007
      Type
      Conference Contribution
      Publisher
      Elsevier Science Publishers B.V.
      Rights
      This is an author’s version of an article published in the journal: Knowledge-Based Systems, © copyright 2006 Elsevier B.V.
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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