[1]朱志宾,丁世飞.基于TWSVM的图像分类[J].南京师大学报(自然科学版),2014,37(03):8.
　Zhu Zhibin,Ding Shifei.Image Classification Based on Twin Support Vector Machines[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(03):8.
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基于TWSVM的图像分类()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第37卷
期数:
2014年03期
页码:
8
栏目:
计算机科学
出版日期:
2014-09-30

文章信息/Info

Title:
Image Classification Based on Twin Support Vector Machines
作者:
朱志宾1; 丁世飞1; 2
(1.中国矿业大学计算机科学与技术学院,江苏 徐州 221116)(2.中国科学院计算技术研究所智能信息处理重点实验室,北京 100190)
Author(s):
Zhu Zhibin1; Ding Shifei1; 2
(1.School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China)(2.Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)
关键词:
图像分类; 支持向量机; 孪生支持向量机; 特征提取
Keywords:
image classification; Support Vector Machine; Twin Support Vector Machines; feature extraction
分类号:
TP181
文献标志码:
A
摘要:
图像分类技术是图像数据处理中最重要的技术之一.支持向量机是基于统计学习理论而提出的机器学习算法,在样本数少的时候能达到很好的分类效果.孪生支持向量机是基于支持向量机而提出来的,其性能优于支持向量机.通过提取彩色图像的颜色特征与纹理特征,利用孪生支持向量机与支持向量机对这些特征向量进行分类,孪生支持向量机的分类准确率与稳定性都高于支持向量机.
Abstract:
The image classification is one of the most important technologies of image data processing.Support Vector Machine is a machine learning algorithm based on statistical learning theory,which can achieve a good classification results in the small sample size.Twin Support Vector Machines is based on Support Vector Machine,which is superior to Support Vector Machine.By extracting color features and texture features of images,using Twin Support Vector Machines and Support Vector Machine to classify these feature vectors,the results shows that the accuracy and stability of Twin Support Vector Machines is higher than Support Vector Machine.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-02-16.
基金项目:国家重点基础研究发展规划(973计划)(2013CB329502)、国家自然科学基金(61379101).
通讯联系人:丁世飞,博士,教授,博士生导师,研究方向:人工智能、模式识别、数据挖掘等.E-mail:dingsf@cumt.edu.cn
更新日期/Last Update: 2014-09-30