SceBoost Learning Algorithm for Feature Selection

Authors:
Min Zhang;Qingsheng Zhu;Feng Liu
Affiliations:
Chongqing University, China;Chongqing University, China;Chongqing University, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Year:
2007

Citing 0
Cited 1

Automatic citrus canker detection from leaf images captured in field

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus foliage. Compared with related feature selection algorithm our method can get improvements in classification accuracy and significantly reduce computation time when reach the same requirements.