计算机工程 ›› 2013, Vol. 39 ›› Issue (5): 266-269.doi: 10.3969/j.issn.1000-3428.2013.05.058

• 图形图像处理 • 上一篇    下一篇

一种基于支持向量机的棉花图像分割算法

陈钦政,赖惠成,王 星,任 磊,刘金帅   

  1. (新疆大学信息科学与工程学院,乌鲁木齐 830046)
  • 收稿日期:2012-04-16 出版日期:2013-05-15 发布日期:2013-05-14
  • 作者简介:陈钦政(1989-),男,硕士研究生,主研方向:机器视觉,数字图像处理,人工智能;赖惠成,教授;王 星、 任 磊、刘金帅,硕士研究生
  • 基金项目:
    新疆维吾尔自治区自然科学基金资助项目(2011211A010)

An Cotton Image Segmentation Algorithm Based on Support Vector Machine

CHEN Qin-zheng, LAI Hui-cheng, WANG Xing, REN Lei, LIU Jin-shuai   

  1. (College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China)
  • Received:2012-04-16 Online:2013-05-15 Published:2013-05-14

摘要: 提出一种基于支持向量机的棉花图像分割算法。将棉花图像分成目标与背景2类。在OHTA颜色空间下提取各类样本像素值,利用支持向量机(SVM)训练带有类别信息的样本。运用最大类间方差(Otsu)法对图像进行预处理,采用训练好的SVM分类器对预处理后的棉花图像进行分割,并使用区域标记法去噪。实验结果表明,该算法可以有效地分割出复杂背景下的棉花,分割准确率达92.3%,分割速度、分割准确率优于直接使用SVM分割图像的方法,分割精度、稳定性优于阈值分割法。

关键词: 棉花分割, OHTA颜色空间, 支持向量机, 最大类间方差法, 去噪

Abstract: A new kind of cotton image segmentation algorithm based on Support Vector Machine(SVM) is proposed. The cotton image is classified into the target and the background. The sample pixel value is extracted from the two categories under OHTA color space. These samples are trained with category information by SVM. Otsu method is used to deal with the image. SVM classifier is trained to segment the cotton image after pretreatment, and the noise is removed by region label. Experimental results show that this method can segment cotton image with the complex background, and the accuracy of segmentation cotton images is as high as 92.3%. Its classify speed is better than directly using SVM to segment the image. The accuracy and stability are better than threshold segmentation method.

Key words: cotton segmentation, OHTA color space, Support Vector Machine(SVM), Otsu method, denoising

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