DocumentCode :
2707484
Title :
An improved random forest classifier for image classification
Author :
Xu, Baoxun ; Ye, Yunming ; Nie, Lei
Author_Institution :
Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China
fYear :
2012
fDate :
6-8 June 2012
Firstpage :
795
Lastpage :
800
Abstract :
This paper proposes an improved random forest algorithm for image classification. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is image data. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to classify image data with a large number of object categories. With the new feature weighting method for subspace sampling and tree selection method, we can effectively reduce subspace size and improve classification performance without increasing error bound. Experimental results on image datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman´s method.
Keywords :
feature extraction; image classification; trees (mathematics); Breiman method; feature weighting method; high dimensional data; image classification; image datasets; improved random forest classifier; representative data; subspace sampling; tree selection method; Accuracy; Classification algorithms; Correlation; Decision trees; Radio frequency; Training data; Vegetation; Random forest; decision tree; image classification; random subspace;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2012 International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4673-2238-6
Electronic_ISBN :
978-1-4673-2236-2
Type :
conf
DOI :
10.1109/ICInfA.2012.6246927
Filename :
6246927
Link To Document :
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