Abstract
In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix (GLCM), has been proposed and used in automatic identification systems. However, according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor (KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.
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Foundation item: the Yunnan Applied Basic Research Projects (No. 2016FD039), and the Talent Cultivation Project in Yunnan Province (No. KKSY201503063)
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Xue, A., Li, F. & Xiong, Y. Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block. J. Shanghai Jiaotong Univ. (Sci.) 24, 220–225 (2019). https://doi.org/10.1007/s12204-018-2013-y
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DOI: https://doi.org/10.1007/s12204-018-2013-y
Key words
- automatic identification
- butterfly species
- gray-level co-occurrence matrix (GLCM)
- features of image block