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Computer
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Information Sciences: an International Journal
Superimposed image description and retrieval for fish species identification
ECDL'09 Proceedings of the 13th European conference on Research and advanced technology for digital libraries
A flexible method for localisation and classification of footprints of small species
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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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.