Authors:
Pornntiwa Pawara
1
;
Emmanuel Okafor
1
;
Olarik Surinta
2
;
Lambert Schomaker
1
and
Marco Wiering
1
Affiliations:
1
University of Groningen, Netherlands
;
2
Multi-Agent Intelligent Simulation Laboratory (MISL), Thailand
Keyword(s):
Convolutional Neural Network, Deep Learning, Bags of Visual Words, Local Descriptor, Plant Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Classification
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Understanding
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Object Recognition
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
Abstract:
The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap, and Folio. To achieve this, we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset, Folio.