Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

Application Research of Ensemble Learning Algorithm in Image Annotation

Authors
Zhenxiang Lin, Jinlin Guo, Songyang Lao
Corresponding Author
Zhenxiang Lin
Available Online April 2017.
DOI
10.2991/fmsmt-17.2017.28How to use a DOI?
Keywords
Ensemble learning algorithm, Image annotation, Machine learning
Abstract

Automatic image annotation is a fundamental and challenging task in the field of image retrieval. This paper introduces the concept and process of random forest algorithm. According to the characteristics of the current annotation model, the random forest algorithm is applied in image annotation field. The experimental results show that the machine learning algorithm used in this paper is more effective than traditional algorithms, which has a higher accuracy and a short processing time.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-331-9
ISSN
2352-5401
DOI
10.2991/fmsmt-17.2017.28How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhenxiang Lin
AU  - Jinlin Guo
AU  - Songyang Lao
PY  - 2017/04
DA  - 2017/04
TI  - Application Research of Ensemble Learning Algorithm in Image Annotation
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 131
EP  - 134
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.28
DO  - 10.2991/fmsmt-17.2017.28
ID  - Lin2017/04
ER  -