Tobacco-leaf Ranking Method Based on Fisher Discriminant Analysis and Ensemble Learning
Intelligent tobacco-leaf ranking is crucial for the quality of cigarette production.To further improve the accuracy of tobacco-leaf ranking,a method of jointly utilizing three leaf views of front,back and perspective is proposed.For each of the three views,texture,color and shape features of tobacco leaves are extracted as the input of the model,and then the feature dimensionali-ty is reduced by linear discriminant analysis.SVM is used as the base classifier,and multiple SVMs are integrated using bagging method to form a ensemble learning based tobacco ranking model.To integrate the features from the three views,the ensemble mod-el is further improved in ranking accuracy.The experimental results show that for only the front view the average accuracy of the pro-posed method achieves 71.39%on the five real tobacco-leaf datasets,which outperforms several existing methods.After fusing joint features of the three views,the accuracy is up amount to 74.8%.
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