Evaluation of texture features for automatic detecting butterfly species using extreme learning machine

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dc.authoridTekin, Ramazan/0000-0003-4325-6922
dc.authoridkayci, lokman/0000-0003-4372-5717
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKayci, Lokman
dc.contributor.authorTekin, Ramazan
dc.contributor.authorErtugrul, O. Faruk
dc.date.accessioned2024-12-24T19:28:11Z
dc.date.available2024-12-24T19:28:11Z
dc.date.issued2014
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.
dc.identifier.doi10.1080/0952813X.2013.861875
dc.identifier.endpage281
dc.identifier.issn0952-813X
dc.identifier.issn1362-3079
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84899975015
dc.identifier.scopusqualityQ1
dc.identifier.startpage267
dc.identifier.urihttps://doi.org/10.1080/0952813X.2013.861875
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6949
dc.identifier.volume26
dc.identifier.wosWOS:000335197300008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Experimental & Theoretical Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectbutterfly identification
dc.subjectexpert system
dc.subjectgrey level co-occurrence matrix
dc.subjectlocal binary patterns
dc.subjecttexture analysis
dc.subjectextreme learning machine
dc.titleEvaluation of texture features for automatic detecting butterfly species using extreme learning machine
dc.typeArticle

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