基于模糊粗糙集的图像自动分类研究

Abstract

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

CLC Number:

TG156

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.

References

[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.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Automatic image classification based on fuzzy-rough set

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 4

Metrics

Comments

Recommended 0

[1]	CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng. Image classification methods based on space vector model [J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[2]	WANG Ying, GUO Lei, LIANG Nan. Classification algorithm of hyperspectral images based on kernel entropy analysis [J]. , 2012, (06): 1597-1601.
[3]	LIU Ping-ping, ZHAO Hong-wei, GENG Qing-tian, DAI Jin-bo. Image classification method based on local feature and visual cortex recognition mechanism [J]. 吉林大学学报(工学版), 2011, 41(05): 1401-1406.
[4]	Cao Chun-hong Zhang Bin,Li Xiao-lin . Medical image classification technology based on fuzzy support vector machine [J]. 吉林大学学报(工学版), 2007, 37(03): 630-0633.