Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

Feature Evaluation in Fine-gain of Leaf

Authors
Zhanhao Chen, Shan Xu, Yixiong Zou, Hualong Zhang, Zhu Zhang, Yue Li, Wei Wang
Corresponding Author
Zhanhao Chen
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.35How to use a DOI?
Keywords
leaf recognition; random forest; feature evaluation
Abstract
In order to compare the value of several features involving leaf retrieval, we design an approach to evaluate 37 features about leaf’s contour, content and texture. Random forest algorithm is employed to rank these features’ contribution to leaf categorization. To forming the optimum features combination, we get the highest retrieval accuracy by gradually adding the most valuable features and depict the relationship between accuracy and feature number. Combined with the time analysis, different features group could be adopted for efficiency requirement. The leaf samples are from Taiwan and ICL database.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Machinery, Materials and Computing Technology
Part of series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.35How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhanhao Chen
AU  - Shan Xu
AU  - Yixiong Zou
AU  - Hualong Zhang
AU  - Zhu Zhang
AU  - Yue Li
AU  - Wei Wang
PY  - 2016/03
DA  - 2016/03
TI  - Feature Evaluation in Fine-gain of Leaf
BT  - 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 186
EP  - 192
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.35
DO  - https://doi.org/10.2991/icmmct-16.2016.35
ID  - Chen2016/03
ER  -