کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
403233 677071 2007 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic species identification of live moths
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Automatic species identification of live moths
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 20, Issue 2, March 2007, Pages 195–202
نویسندگان
, ,