期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

垃圾图像判别中的特征提取与选择研究 被引量：1

Research of feature extraction and selection for spam image identification
在线阅读 下载PDF
导出
摘要 对垃圾图像判别问题中的特征提取和特征选择研究现状进行了总结。从特征的可区分性、鲁棒性和提取效率三个方面比较了垃圾图像判别中的主要特征,分析了特征的优缺点。结合分类学习算法、仿真实验结果,对已有的主要特征选择和分析方法进行比对,为进一步研究特征提取、特征选择方法,提高垃圾图像分类器的性能和效率提供有价值的参考。 This paper was a review of the methods of feature extraction and feature selection for spam image identification. It compared the characteristics of the extracted features based on discriminability, robustness, and efficiency. Explained the strengths and weaknesses of different features. Combined with the related classification learning algorithms and experimental resuits, analyzed the feature selection methods. This survey is a valuable reference for further research on feature extraction and selection to improve the performance of spam image detection.
作者 程红蓉 秦志光 万明成 曾志华
机构地区 电子科技大学计算机科学与工程学院
出处 《计算机应用研究》 CSCD 北大核心 2009年第6期2001-2003,共3页 Application Research of Computers
基金 国家高技术研究发展计划资助项目(2006AA01Z411)
关键词 垃圾图像 特征提取 特征选择 分类器 spam image feature extraction feature selection classitication
分类号 TP309 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献24

  • 1Image spam[ EB/OL]. http ://en. wikipedia. org/wiki/Image_spam.
  • 2GANESHANANDAM S, KRZANOWSKI W. On selecting variables and assessing their performance in linear discriminate analysis [ J ]. Australian Journal of Statistics, 1989,31 (3) :433-447.
  • 3边肇棋，张学工．模式识别[M]．2版．北京：清华大学出版社，2000．
  • 4FORTUNA J, CAPSON D. Improved vector classification using PCA and ICA feature space modification[ J]. Pattern Recognition,2004, 37(6) :1117-1129.
  • 5CAO L J, CHUA K S, CHONG W K, et al. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine [ J]. Neurocomputing ,2003,55 (1/2) :321-336.
  • 6WANG Zhe ,JOSEPHSON W, LV Qin. Filtering image spam with near-duplicate detection[ C]//Proc of the 4th Conference on E-mail and AntiSpam. 2007.
  • 7BYUN B, LEE C H, WEBB S. A discriminative classifier learning approach to image modeling and spare image identification[ C]//Proc of the 4th Conference on E-mail and AntiSpam. 2007.
  • 8ARADHYE H B, MYERS G K, HERSON J A. Image analysis for efficient categorization of image-based spam e-mail[ C]//Proc of the 8th International Conference on Document Analysis and Recognition. Seoul : IEEE Computer Society ,2005:914-918.
  • 9KRASSER S, TANG Yu-chun, GOULD J. Identifying image spam based on header and file properties using C4.5 decision trees and support vector machine learning[C]//Proc of IEEE Workshop on Information Assurance and Security Workshop. 2007.
  • 10DREDZE M,GEVARYAHU R,BACHRACH A E. Learning fast classifters for image spam[ C]//Proc of the 4th Conference on E-mail and AntiSpam. California: ACM Press,2007 : 255 - 261.

二级参考文献8

共引文献11

同被引文献15

  • 1MessageLabs Intelligence 2010年9月网络安全报告[ EB/OL ]. (2010-09) [ 2010- 10- O1 ]. http ://www. messagelabs, corn/down-load. get? filename = MLI_2010 09_September FINAL_EN. PDF.
  • 2Image spam blog[EB/OL]. (2010-08-16) [2010-09-15]. http:// www. symantec, com/connect/blogs/ image- spare.
  • 3FUMERA G, PILLA1 I, ROLl F. Spare filtering based on the analysis of text information embedded into images [ J ]. Journal of Machine Learning Research, 2006, 7: 2699-2720.
  • 4DREDZE M, GEVARYAHU R, EL1AS-BACHRACH A. Learning fast classifiers fur image spare [ C ]//Proc of the 4th Conference on Email and AntiSpare. Mountain View, CA: [s. n. ], 2007:487-493.
  • 5KEOGH E, LONARDI S, RATANAMAHATANA C A. Towards parameter-free data mining[C ]//Proc of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2004:260-215.
  • 6CHI Z, KONG Jun. Image content classification using a block Kolmogorov complexity measure[ C ] // Proc of the 4th International Conference on Signal Processing. Beijing: IEEE Communications Society,1998:1185-1188.
  • 7KOLMOGOROV A N, SOLOMONOFF R, CHAITIN G. Three approaches to the quantitative definition of information [ J ]. Problems of Information Transmission, 1965,1 (1) :1-7.
  • 8L1 Ming, VITANYI P. An Jntroduction to Kolmogorov complexity and its applications[M]. 3rd ed. New York: SpringerVerlag,2008.
  • 9LIMing, CHEN Xin, LI Xin, et al. The similarity metric[J]. IEEE Trans on Information Theory,2004,50 ( 12 ) : 3250- 3:264.
  • 10LIMing, SLEEP R. Melody classification using a similarity metric based on Kolmogorov complexity[ C ]//Proc of Sound and Music Computing. Paris: [s. n. ] , 2004: 126-129.

引证文献1

二级引证文献1

计算机应用研究
2009年 第6期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部