Open AccessEvaluation of basic convolutional neural network and bag of features for leaf recognition
Author(s) -
Nurul Fatihah Sahidan,
Ahmad Khairi Juha,
Zarina Bibi İbrahim
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v14.i1.pp327-332
Subject(s) - convolutional neural network , computer science , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , scalability , feature extraction , representation (politics) , image (mathematics) , set (abstract data type) , database , philosophy , linguistics , politics , political science , law , programming language
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.