Title
Fine-Grained Hierarchical Classification of Plant Leaf Images Using Fusion of Deep Models
Abstract
A fine-grained plant leaf classification method based on the fusion of deep models is described. Complementary global and patch-based leaf features are combined at each hierarchical level (genus and species) by pre-trained CNNs. The deep models are adapted for plant recognition by using data augmentation techniques to face the problem of plant classes with very few samples for training in the available imbalanced dataset. Experimental results have shown that the proposed coarse-to-fine classification strategy is a very promising alternative to deal with the low inter-class and high intra-class variability inherent to the problem of plant identification. The proposed method was able to surpass other state-of-the-art approaches on the ImageCLEF 2015 plant recognition dataset in terms of average classification scores.
Year
DOI
Venue
2018
10.1109/ICTAI.2018.00011
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
Field
DocType
Plant Recognition, Hierarchical Classification, Deep Models, Fine grained
Task analysis,Pattern recognition,Computer science,Image representation,Fusion,Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Plant identification
Conference
ISSN
ISBN
Citations 
1082-3409
978-1-5386-7450-5
1
PageRank 
References 
Authors
0.35
10
5
Name
Order
Citations
PageRank
Voncarlos M. Araújo110.35
Alceu Britto29418.30
André L. Brun310.35
Alessandro L. Koerich452539.59
Luiz S. Oliveira547647.22