NDLI: Forest species recognition using macroscopic images

Content Provider	Springer Nature Link
Author	Filho, Pedro L. Paula Oliveira, Luiz S. Nisgoski, Silvana Britto, Alceu S.
Copyright Year	2014
Abstract	The recognition of forest species is a very challenging task that generally requires well-trained human specialists. However, few reach good accuracy in classification due to the time taken for their training; then they are not enough to meet the industry demands. Computer vision systems are a very interesting alternative for this case. The construction of a reliable classification system is not a trivial task, though. In the case of forest species, one must deal with the great intra-class variability and also the lack of a public available database for training and testing the classifiers. To cope with such a variability, in this work, we propose a two-level divide-and-conquer classification strategy where the image is first divided into several sub-images which are classified independently. In the lower level, all the decisions of the different classifiers, trained with different features, are combined through a fusion rule to generate a decision for the sub-image. The higher-level fusion combines all these partial decisions for the sub-images to produce a final decision. Besides the classification system we also extended our previous database, which now is composed of 41 species of Brazilian flora. It is available upon request for research purposes. A series of experiments show that the proposed strategy achieves compelling results. Compared to the best single classifier, which is a SVM trained with a texture-based feature set, the divide-and-conquer strategy improves the recognition rate in about 9 percentage points, while the mean improvement observed with SVMs trained on different descriptors was about 19 percentage points. The best recognition rate achieved in this work was 97.77 %.
Starting Page	1019
Ending Page	1031
Page Count	13
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	25
Issue Number	4
e-ISSN	14321769
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-01-28
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Textural descriptors Fusion of classifiers Two-level classification strategy Forest species classification Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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