基于模糊粗糙集的图像自动分类研究

吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 209-212.

基于模糊粗糙集的图像自动分类研究

陈载清, 石俊生, 白凤翔

云南师范大学颜色与图像视觉实验室,昆明 650092

收稿日期:2012-05-03 发布日期:2013-06-01
通讯作者: 石俊生(1960-),男,博士生导师.研究方向:颜色科学,图像处理.E-mail:shijs@ynnu.edu.cn E-mail:shijs@ynnu.edu.cn
作者简介:陈载清(1978-),男,博士研究生.研究方向:颜色与图像视觉.E-mail:zaiqing.chen@gmail.com
基金资助:
国家自然科学基金项目(60668001);云南教育厅重点项目(ZD2009006).

Automatic image classification based on fuzzy-rough set

CHEN Zai-qing, SHI Jun-sheng, BAI Feng-xiang

Color & Image Vision Lab., Yunnan Normal University, Kunming 650092, China

Received:2012-05-03 Published:2013-06-01

摘要/Abstract

摘要：

如何使用图像底层特征有效表达高层语义是实现图像自动分类难以逾越的鸿沟。本文将模糊粗糙集理论引入图像自动分类,在使用图像底层特征表达高层语义的图像自动分类过程中,把高维特征向量处理、合适的描述符集合选择难题转换为模糊决策表,使用图像语义贴近度概念来检验图像特征属性间的数据依赖关系,以达到属性约简,剔除冗余信息和图像分类规则推导的目的,并定义了图像类别隶属度函数对图像进行分类。实验结果表明该图像分类系统的分类正确率达81.7%,说明该方法具有很好的精确性和有效性,能较好地实现图像自动分类。

关键词: 图像分类, 模糊粗糙集, 语义贴近度, 隶属度

Abstract:

There is a gap between low-level feature of image and high-level semantic understanding of users in the automatic image classification.The fuzzy-rough set theory was introduced into automatic image classification.During the mapping from low-level visual feature to high-level semantic feature,the problems of high-dimensional feature vector processing and the appropriate choice of descriptors in image classification processing were converted to fuzzy decision table,and the concept of the semantic proximity was used to verify the dependent relations of image attributes for attribute reduction,which could eliminate the redundant information and deduce the rule of image classification.In the end,the category of an image was determined by a membership degree function.An image classification system was developed and the accuracy of the classification results was 81.7%,experimental results show that the method has good accuracy and effectiveness,and can achieve a better image automatic classification.

Key words: image classification, fuzzy-rough set, semantic proximity, membership degree

中图分类号:

TG156

陈载清, 石俊生, 白凤翔. 基于模糊粗糙集的图像自动分类研究[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 209-212.

CHEN Zai-qing, SHI Jun-sheng, BAI Feng-xiang. Automatic image classification based on fuzzy-rough set[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 209-212.

参考文献

[1] Cristianini N,Shawe-Taylor J.An introduction to support Vector Machines:And other kernel-based learning methods [M].Cambridge Univ Pr,2000.

[2] Haralick R M,Shanmugam K,Dinstein I H.Textural features for image classification [J].Systems,Man and Cybernetics,IEEE Transactions on,1973,3(6):610-621.

[3] Cheng Y C,Chen S Y.Image classification using color,texture and regions [J].Image and Vision Computing,2003,21(9):759-776.

[4] Zhang J,Guo X C,Jin C,et al.Image classification method based on feature complement ratio matrix [J].Computer Engineering,2011,37(4):230-231.

[5] Wan H L,Chowdhury M.Image semantic classification by using SVM [J].Journal of Software,2012,14(11):1891-1899.

[6] 肖政宏,王家廞.一种新的基于目标和特征的图像分类框架 [J].计算机应用与软件,2006,23(5):105-107. Xiao Zheng-hong,Wang Jia-xin.A novel image classification scheme based on object and feature [J].Computer Applications and Software,2006,23(5):105-107.

[7] Duda R O,Hart P E,Stork D G.Pattern classification [M].Wiley-Interscience,2000.

[8] Vapnik V.Statistical learning theory[M].New York:Wiley,1998.

[9] 王松,王卫红,秦绪佳.一种新的图像分类方法研究 [J].计算机应用研究,2006,23(12):323-325. Wang Song,Wang Wei-hong,Qin Xu-jia.New image classification application research [J].Application Research of Computers,2006,23(12):323-325.

[10] 王丽,冯山.基于模糊粗糙集的两种属性约简算法 [J].计算机应用,2006,26(3):635-637. Wang Li,Feng Shan.Two attribute reduction algorithms based on fuzzy-rough set [J].Computer Application,2006,26(3):635-637.

[11] 张雪英.基于粗糙集理论的文本自动分类研究[D].南京:南京理工大学,2005. Zhang Xue-ying.Automatic text categorization based on rough set theory[D].Nanjing:Nanjing University of Science and Technology,2005.

[12] 刘惟一.基于语义贴近度的模糊数据依赖 [J].计算机学报,1997,20(8):725-730. Liu Wei-yi.The fuzzy data dependencies based on the semantic proximity [J].Chinese J.Computers,1997,20(8):725-730.

[13] 刘惟一.数据模型 [M].北京:科学出版社,2001.

相关文章 11

[1]	陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[2]	范敏, 韩琪, 王芬, 宿晓岚, 徐浩, 吴松麟. 基于多层次特征表示的场景图像分类算法[J]. 吉林大学学报(工学版), 2017, 47(6): 1909-1917.
[3]	邢海燕, 葛桦, 李思岐, 杨文光, 孙晓军. 基于模糊隶属度最大似然估计的焊缝隐性缺陷磁记忆信号识别[J]. 吉林大学学报(工学版), 2017, 47(6): 1854-1860.
[4]	宋大凤, 李广含, 张琳, 潘冰, 曾小华, 彭宇君, 王庆年. 模糊逻辑在混合动力汽车电机故障检测中的应用[J]. 吉林大学学报(工学版), 2016, 46(2): 354-359.
[5]	陈涛, 邓辉舫, 刘靖. 基于密度聚类和多示例学习的图像分类方法[J]. 吉林大学学报(工学版), 2014, 44(4): 1126-1134.
[6]	齐滨, 赵春晖, 王玉磊. 基于支持向量机与相关向量机的高光谱图像分类[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 143-147.
[7]	王瀛, 郭雷, 梁楠. 基于核熵成分分析的高光谱遥感图像分类算法[J]. , 2012, (06): 1597-1601.
[8]	孙文邦^1,2,陈贺新¹,唐海燕²,于光². 基于二维模糊熵的图像非监督变化检测[J]. 吉林大学学报(工学版), 2011, 41(05): 1461-1467.
[9]	刘萍萍^1,2,赵宏伟^1,2,耿庆田¹,戴金波¹. 基于局部特征和视皮层识别机制的图像分类[J]. 吉林大学学报(工学版), 2011, 41(05): 1401-1406.
[10]	赵一兵，王荣本，李琳辉，郭烈 . 基于D-S证据理论的障碍目标身份识别[J]. 吉林大学学报(工学版), 2008, 38(06): 1295-1299.
[11]	曹春红，张斌，李小琳 . 基于模糊支持向量机的医学图像分类技术[J]. 吉林大学学报(工学版), 2007, 37(03): 630-0633.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed