纹理特征和SIFT特征的茶叶品质分级  

Combination of Texture and SIFT Feature for Grading of Tea Quality

作　　者：陈景波[1] 靳小波[2] 

机构地区：[1]常熟理工学院电气与自动化工程学院,江苏常熟215500 [2]河南工业大学信息科学与工程学院,河南郑州450001

出　　处：《常熟理工学院学报》2013年第2期100-103,共4页Journal of Changshu Institute of Technology

摘　　要：当前的茶叶分级研究主要基于纹理特征构造分类器,但易受采样过程中的光照、噪声影响.本文提出了结合经典的SIFT(Scale-invariant feature transform)特征描述子在自然光条件下的茶叶分级问题,并使用多类AdaBoost算法对样本进行分类.单幅图像的提取结果显示,SIFT特征对带瑕疵的图片仍具有很好的描述能力.在采集的90幅3级茶叶样本上的实验结果显示,纹理特征+SIFT特征取得了比单组特征更好的分类性能.Much research work focuses on the texture feature of the image in the tea grading, but it is prone to the illumination and the noise during the sampling. This paper proposes the combination of the texture and the SIFT features for the recognition of the tea grades under the condition of the natural light, where multi-class Ad-aBoost is used for the classification. The extracted images show that it is robust to the image noise. Moreover, the cross-fold results on 3 categories tea image dataset including 90 examples demonstrate that the texture ＋ SIFT achieves a better performance than one group of the features alone.

关 键 词：纹理特征 SIFT特征 茶叶分级 ADABOOST算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]