Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/23146
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Strezoski, Gjorgji | en_US |
dc.contributor.author | Stojanovski, Dario | en_US |
dc.contributor.author | Dimitrovski, Ivica | en_US |
dc.contributor.author | Madjarov, Gjorgji | en_US |
dc.date.accessioned | 2022-09-28T09:03:33Z | - |
dc.date.available | 2022-09-28T09:03:33Z | - |
dc.date.issued | 2015 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/23146 | - |
dc.description.abstract | Our 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.ispartof | Proceedings of ICT Innovations 2015 Conference Web Proceedings | en_US |
dc.subject | deep learning, SVM, support vector machines, plant images, plantCLEF, CNN | en_US |
dc.title | Deep learning and support vector machine for effective plant identification | en_US |
dc.type | Proceeding article | en_US |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
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deeplrn.pdf | 18.42 MB | Adobe PDF | View/Open |
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