Tea Leaves Classification Based on Texture Analysis

摘要：

　　An SVM with texture analysis-based feature extraction classification method is presented for identification of fresh tea leaves in this paper.This method is proved to be very efficient and effective in the identification of fresh tea leaves through real experiments.First,the texture characteristic parameters of tea leave images are obtained by texture feature extraction.After that,different categories of fresh tea leaves are identified through SVM training.These texture parameters for texture classification include energy,correlation,and contrast obtained from graylevel co-occurrence matrix(GLCM).Experimental results show that the use of SVM for classification of tea leaves can achieve very good results,and the successful classification rate can be as high as 83%.

关键词： Texture analysis GLCM SVM Tea leaves classification

作者: Zhe Tang Fang Qi Yi Zhou Fangfang Pan Jianyong Zhou

作者单位: School of Information Science and Engineering,Central South University,Changsha,China Singapore Institute of Technology,10 Dover Drive,Singapore,Singapore Changsha Xiangfeng Tea Machinery Manufacturing Co.,Ltd,Changsha,China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 353-360

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）