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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

ISBN: 978-989-758-222-6

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.

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Paper citation in several formats:
Pawara, P.; Okafor, E.; Surinta, O.; Schomaker, L. and Wiering, M. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 479-486. DOI: 10.5220/0006196204790486

@conference{icpram17,
author={Pornntiwa Pawara. and Emmanuel Okafor. and Olarik Surinta. and Lambert Schomaker. and Marco Wiering.},
title={Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={479-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006196204790486},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition
SN - 978-989-758-222-6
AU - Pawara, P.
AU - Okafor, E.
AU - Surinta, O.
AU - Schomaker, L.
AU - Wiering, M.
PY - 2017
SP - 479
EP - 486
DO - 10.5220/0006196204790486

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