Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

Screening of Tobacco's Effective Image Features Based on a Semi -supervised Clustering

Authors
Ge Jin, Lin Qi, Hang Li
Corresponding Author
Ge Jin
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.59How to use a DOI?
Keywords
Clustering; Discriminant function; Feature selection; Support vector machine; Tobacco classification
Abstract
In order to reduce the number of extracting tobacco's image features and the computational complexity of hierarchical model, and to increase the speed and accuracy of tobacco classification, this paper presents a feature selection method that based on the semi - supervised clustering. First, define the discriminate function R which can distinguish good features from bad features, and delete bad features according to the R-value. Then set up 42 levels' SVM hierarchical model using all features and screened features. Experimental results show that the feature selection method constructed in this paper can select effective features, which can raise the tobacco's classification speed under the premise of correct discrimination.
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Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.59How 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  - Ge Jin
AU  - Lin Qi
AU  - Hang Li
PY  - 2016/12
DA  - 2016/12
TI  - Screening of Tobacco's Effective Image Features Based on a Semi -supervised Clustering
BT  - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 281
EP  - 284
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.59
DO  - https://doi.org/10.2991/mcei-16.2016.59
ID  - Jin2016/12
ER  -