Evaluation of basic convolutional neural network and bag of features for leaf recognition
This paper presents the evaluation of basic Convolutional Neural Network (CNN) and Bag of Features (BoF) for Leaf Recognition. In this study, the performance of basic CNN and BoF for leaf recognition using a publicly available dataset called Folio dataset has been investigated. CNN has proven its po...
| Published in: | Indonesian Journal of Electrical Engineering and Computer Science |
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| Main Author: | |
| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061127709&doi=10.11591%2fijeecs.v14.i1.pp327-332&partnerID=40&md5=58b4c995e5619069527b2c71dcee6ce2 |
| Summary: | This paper presents the evaluation of basic Convolutional Neural Network (CNN) and Bag of Features (BoF) for Leaf Recognition. In this study, the performance of basic CNN and BoF for leaf recognition using a publicly available dataset called Folio dataset has been investigated. CNN has proven its powerful feature representation power in computer vision. The same goes with BoF where it has set new performance standards on popular image classification benchmarks and has achieved scalability breakthrough in image retrieval. The feature that is being utilized in the BoF is Speeded-Up Robust Feature (SURF) texture feature. The experimental results indicate that BoF achieves better accuracy compared to basic CNN. © 2019 Institute of Advanced Engineering and Science. All rights reserved. |
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| ISSN: | 25024752 |
| DOI: | 10.11591/ijeecs.v14.i1.pp327-332 |