关键词: 支持向量数据描述, 异物检测, 烟叶样本, HV分量, 分类识别

Abstract: It is difficult to fully collect foreign body sample in detecting foreign bodies from tobacco. A detection method based on Support Vector Data Description (SVDD) was proposed. Thus a one-class classifier can be developed by using tobacco samples soly. RGB and HSV of tobacco and several typical foreign bodies were firstly extracted; then the HV component was used as eigenvector. A developed SVDD classifier was applied to distinguish foreign bodies from tobacco by inputting the HV eigenvector. Finally through the Receiver Operating Characteristic (ROC) curve, the SVDD classifier was compared with three other methods in classification effect. The experimental results show that by adopting feature extraction with HV component, the data dimension was reduced and a higher computation efficiency was achieved. The SVDD classifier has a stronger classification ability and higher efficiency, which could distinguish foreign bodies from tobacco better.

Key words: Support Vector Data Description (SVDD), foreign body detection, tobacco sample, HV component, classification