A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression
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Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We present an application of image-processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. Grey-level co-occurrence matrix (GLCM) matrices are utilised to evaluate the surface texture features of butterflies' wings, which is an important character for identification. Eleven textural features were extracted from butterfly images and characterised by the texture average in four directions (0 degrees, 45 degrees, 90 degrees and 135 degrees) and distances (d = 1, 2, 3 and 4 pixels). We used 190 butterfly images belonging to 19 different species of the family Pieridae. The identification accuracy of the GLCM+MLR was 96.3% with tenfold cross validation. The methodology presented here classified the butterflies effectively. These findings suggest that the proposed MLR algorithm and GLCM texture features technique are feasible for the identification and classification of butterfly species.
Açıklama
Anahtar Kelimeler
Butterfly identification, expert system, grey-level co-occurrence matrix, multinomial logistic regression, texture analysis
Kaynak
Zoology in The Middle East
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
60
Sayı
1
