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%.
XUE Ankang (薛安康), LI Fan* (李凡), XIONG Yin (熊吟)
. Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(2)
: 220
-225
.
DOI: 10.1007/s12204-018-2013-y
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