Oak Leaf Classification: An Analysis of Features and Classifiers

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dc.authorid0000-0003-0896-3058
dc.authorid0000-0001-7947-5508
dc.authorscopusid36241785000
dc.authorscopusid57209736514
dc.authorscopusid9744137200
dc.authorscopusid57209734448
dc.authorscopusid37113831300
dc.authorwosidEnsari, Tolga/D-3799-2019
dc.contributor.authorKaya, Heysem
dc.contributor.authorKeklik, İlhan
dc.contributor.authorEnsari, Tolga
dc.contributor.authorAlkan, Fatih
dc.contributor.authorBiricik, Yağmur
dc.date.accessioned2022-05-11T14:15:55Z
dc.date.available2022-05-11T14:15:55Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionInternational Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEY
dc.description.abstractAutomatic classification of trees from leaves is a popular computer vision/machine learning task and has important applications in monitoring of forest wealth. While the final aim is preparing an application, which is capable of visual signal processing and classification, in this paper we present a new oak leaf dataset and preliminary results for classification of 8 types of oak trees. The novelties include comparative analysis of a small set of hand-crafted geometric features and popularly used high-dimensional appearance features, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG). We further compare commonly used Support Vector Machines (SVM) classifier with a recently popular, fast and robust learner called Extreme Learning Machines (ELM). Results indicate that a small set of geometric features reach an accuracy of 75%, while high dimensional appearance features can boost the performance up to92%.
dc.description.sponsorshipIEEE Turkey Sect, IEEE EMB, Erasmus+, Europass
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.scopus2-s2.0-85068570413
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6122
dc.identifier.wosWOS:000491430200057
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (Ebbt)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClassificaiton
dc.subjectLBP
dc.subjectHOG
dc.subjectExtreme Learning Machines (ELM)
dc.subjectExtreme Learning-Machine
dc.titleOak Leaf Classification: An Analysis of Features and Classifiers
dc.typeConference Object

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