Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/23146
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dc.contributor.authorStrezoski, Gjorgjien_US
dc.contributor.authorStojanovski, Darioen_US
dc.contributor.authorDimitrovski, Ivicaen_US
dc.contributor.authorMadjarov, Gjorgjien_US
dc.date.accessioned2022-09-28T09:03:33Z-
dc.date.available2022-09-28T09:03:33Z-
dc.date.issued2015-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/23146-
dc.description.abstractOur planet is blooming with vegetation that consists of hundreds of thousands of plant species. Each and every one species is unique in its own way, thus enabling people to distinguish one plant from another. Distinguishing plant species is a non trivial task, in fact, it is challenging even for renowned botanists with lots of years of experience in the field. Having in mind the complexity of the task, in this paper we present a system for plant species identification based on Convolutional Neural Networks (CNN’s) and Support Vector Machines (SVM’s). The combination of these two approaches for both feature generation and classification results in a powerful plant identification system. Additionally we report state of the art results using this approach, as well as comparison with other types of approaches on the same dataset.en_US
dc.relation.ispartofProceedings of ICT Innovations 2015 Conference Web Proceedingsen_US
dc.subjectdeep learning, SVM, support vector machines, plant images, plantCLEF, CNNen_US
dc.titleDeep learning and support vector machine for effective plant identificationen_US
dc.typeProceeding articleen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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