Paper Info
Title
Forest Species Recognition Based on Ensembles of Classifiers
Abstract
Recognition of forest species is a very challenging task thanks to the great intra-class variability. To cope with such a variability, we propose a multiple classifier system based on a two-level classification strategy and microscopic images. By using a divide-and-conquer approach, an image is first divided into several sub-images which are classified independently by each classifier. In a first fusion level, partial decisions for the sub-images are combined to generate a new partial decision for the original image. Then, the second fusion level combines all these new partial decisions to produce the final classification of the original image. To generate the pool of diverse classifiers, we used classical texture-based features as well as keypoint-based features. A series of experiments shows that the proposed strategy achieves compelling results. Compared to the best single classifier, a Support Vector Machine (SVM) trained with a keypoint based feature set, the divide-and-conquer strategy improves the recognition rate in about 4 and 6 percentage points in the first and second fusion levels, respectively. The best recognition rate achieved by this proposed method is 98.47%.
Year
DOI
Venue
2018
10.1109/ICTAI.2018.00065
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
Field
DocType
Textural descriptor,multiple classifier systems,fusion rules,forest species recognition
Histogram,Ensembles of classifiers,Task analysis,Pattern recognition,Computer science,Support vector machine,Feature extraction,Feature set,Artificial intelligence,Percentage point,Classifier (linguistics),Machine learning
Conference
ISSN
ISBN
Citations 
1082-3409
978-1-5386-7450-5
0
PageRank 
References 
Authors
0.34
20
4
Name
Order
Citations
PageRank
Jefferson Martins1251.89
Luiz S. Oliveira247647.22
Robert Sabourin390861.89
Alceu Britto49418.30