•  
  •  
 

Abstract

Objective:In order to improve the identification efficiency of cut tobacco.Methods:F-score feature selection method and AdaBoost ensemble learning method were used to recognize cut tobacco components. The texture, color and shape features of cut tobacco were extracted as the input of the model. The feature dimension is reduced by F-score feature selection method, and the support vector machine (SVM) was used as the base classifier, then AdaBoost ensemble learning method was used to get the classification model of cut tobacco.Results:This method could effectively distinguish different components of cut tobacco, and the recognition accuracy of each kind of cut tobacco was more than 95%.Conclusion:AdaBoost ensemble learning method is faster and more convenient than traditional methods, and also safer and more effective.

Publication Date

7-7-2022

First Page

205

Last Page

211

DOI

10.13652/j.spjx.1003.5788.2022.90008

References

[1] 刘晓萍,李斌,于川芳,等.基于近红外光谱的卷烟配方结构识别[J].烟草科技,2006(10):16-18,27.LIU Xiao-ping,LI Bin,YU Chuan-fang,et al.Cigarette formula structure recognition based on near infrared spectroscopy[J].Tobacco Science & Technology,2006(10):16-18,27.
[2] 胡立中,张胜军,余小平,等.均匀设计-PLS-NIR法预测卷烟配方烟丝中梗丝及薄片丝含量[J].中国烟草学报,2010,16(2):26-30.HU Li-zhong,ZHANG Sheng-jun,YU Xiao-ping,et al.Uniform design pls-nir method for predicting the content of cut stem and slice in cigarette formula[J].Acta Tabacaria Sinica,2010,16(2):26-30.
[3] 高震宇,王安,董浩,等.基于卷积神经网络的烟丝物质组成识别方法[J].烟草科技,2017,50(9):68-75.GAO Zhen-yu,WANG An,DONG Hao,et al.Identification method of tobacco material composition based on convolution neural network[J].Tobacco Science & Technology,2017,50(9):68-75.
[4] 钟宇,周明珠,徐燕,等.基于残差神经网络的烟丝类型识别方法的建立[J].烟草科技,2021,54(5):82-89.ZHONG Yu,ZHOU Ming-zhu,XU Yan,et al.Establishment ofcut tobacco type recognition method based on residual neural network[J].Tobacco Science & Technology,2021,54(5):82-89.
[5] 孟金龙,丁超洋,周慧,等.基于SVM的图像分类算法研究[J].数字技术与应用,2017(10):123-124.MENG Jin-long,DING Chao-yang,ZHOU Hui,et al.Researchon image classification algorithm based on SVM[J].Digital Technology & Application,2017(10):123-124.
[6] WANG Rui,LI Chao,WANG Jie,et al.Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images[J].Journal of Neuroscience Methods,2015,241:30-36.
[7] CHEN Jian-shen,KANG Xian-gui,LIU Ye,et al.Median filtering forensics based on convolutional neural networks[J].IEEE Signal Processing Letters,2015,22(11):1 849-1 853.
[8] 胡敏,李梅,汪荣贵.改进的Otsu算法在图像分割中的应用[J].电子测量与仪器学报,2010,24(5):443-449.HU Min,LI Mei,WANG Rong-gui.Application of improved Otsu algorithm in image segmentation[J].Journal of Electronic Measurement and Instrument,2010,24(5):443-449.
[9] 吴晓光,王涤琼,盛慧.一种获取图像区域最小外接矩形的算法及实现[J].计算机工程,2004(12):124-125,142.WU Xiao-guang,WANG Di-qiong,SHENG Hui.An algorithm for obtaining the minimum circumscribed rectangle of image area and its implementation[J].Computer Engineering,2004(12):124-125,142.
[10] 江玉杰,王世航.基于灰度共生矩阵的遥感图像抗旋转性研究[J].电脑知识与技术,2020,16(31):13-16.JIANG Yu-jie,WANG Shi-hang.Research on anti rotation of remote sensing image based on gray level co-occurrence matrix[J].Computer Knowledge and Technology,2020,16(31):13-16.
[11] 罗明俊,万幼川,秦昆.基于灰度分级的图像分割算法的研究[J].地理空间信息,2005(6):9-10,21.LUO Ming-jun,WAN You-chuan,QIN Kun.Research on image segmentation algorithm based on gray level[J].Geospatial Information,2005(6):9-10,21.
[12] 郑永斌,黄新生,丰松江.SIFT和旋转不变LBP相结合的图像匹配算法[J].计算机辅助设计与图形学学报,2010,22(2):286-292.ZHENG Yong-bin,HUANG Xin-sheng,FENG Song-jiang.Image matching algorithm based on SIFT and rotation invariant LBP[J].Journal of Computer-Aided Design & Computer Graphics,2010,22(2):286-292.
[13] 黄祥林,沈兰荪.一种具有旋转不变性的压缩域纹理图像分类方法[J].电子与信息学报,2002(11):1 441-1 446.HUANG Xiang-lin,SHEN Lan-sun.A texture image classification method with rotation invariance in compressed domain[J].Journal of Electronics & Information Technology,2002(11):1 441-1 446.
[14] 侯云,李柏林,刘甲甲,等.基于灰度不变性的扣件定位特征提取方法[J].计算机应用与软件,2015,32(11):193-196.HOU Yun,LI Bo-lin,LIU Jia-jia,et al.Fastener location feature extraction method based on gray invariance[J].Computer Applications and Software,2015,32(11):193-196.
[15] 熊邦书,张晓飞,欧巧凤.基于等价LBP纹理图谱的滚动轴承故障诊断方法[J].南昌航空大学学报(自然科学版),2020,34(4):1-6.XIONG Bang-shu,ZHANG Xiao-fei,OU Qiao-feng.Rolling bearing faultdiagnosis method based on equivalent LBP texture map[J].Journal of Nanchang Hangkong University(Natural Sciences),2020,34(4):1-6.
[16] STRICKER A M A,ORENGO M.Similarity of color images[J].Proceedings of SPIE-The International Society for Optical Engineering,1995,2 420:381-392.
[17] 谢娟英,王春霞,蒋帅,等.基于改进的F-score与支持向量机的特征选择方法[J].计算机应用,2010,30(4):993-996.XIE Juan-ying,WANG Chun-xia,JIANG Shuai,et al.Feature selection method based on improved F-score and support vector machine[J].Journal of Computer Applications,2010,30(4):993-996.
[18] 付秋新.BP-Adaboost集成学习算法在地铁施工沉降预测中的应用研究[J].现代城市轨道交通,2021(5):94-98.FU Qiu-xin.Application of BP AdaBoost integrated learning algorithm in subway construction settlement prediction[J].Modern Urban Transit,2021(5):94-98.
[19] 王璐,吴志刚,任豪杰,等.基于逻辑回归模型的汽车VIN码识别应用研究[J].中原工学院学报,2019,30(4):68-74.WANG Lu,WU Zhi-gang,REN Hao-jie,et al.Application Research on vehicle VIN code recognition based on logistic regression model[J].Journal of Zhongyuan University of Technology,2019,30(4):68-74.
[20] 赵宇鑫,努尔布力,艾壮.基于集成学习投票算法的Android恶意应用检测[J].计算机工程与应用,2020,56(22):74-82.ZHAO Yu-xin,NURBOL,AI Zhuang.Android malicious application detection based on integrated learning voting algorithm[J].Computer Engineering and Applications,2020,56(22):74-82.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.