Evaluation of basic convolutional neural network and bag of features for leaf recognition

Nurul Fatihah Sahidan (Universiti Teknologi MARA)
Ahmad Khairi Juha (Universiti Teknologi MARA)
Zaidah Ibrahim (Universiti Teknologi MARA)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 14, No 1: April 2019

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.