Using visual features for anti-spam filtering

Proceedings - International Conference on Image Processing, ICIP

Volumn 3, Issue , 2005, Pages 509-512

  Wu, Ching Tung ^a   Cheng, Kwang Ting ^a   Zhu, Qiang ^a   Wu, Yi Leh ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b VIMA Technologies Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VIRUSES; ELECTRONIC MAIL; IMAGE ANALYSIS; INTERNET; PROBLEM SOLVING; TEXT PROCESSING;

ANTI SPAM FILTERING; SUPPORT VECTOR MACHINES (SVM); UNSOLICITED COMMERCIAL EMAIL (UCE);

SECURITY OF DATA;

EID: 33749256984     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2005.1530440     Document Type: Conference Paper

References (16)

1. J. Postel. On The Junk Mail Problem. RFC706. November 1975.
2. J. Graham-Gumming. The Spammer's Compendium. http://www.jgc.org/tsc/index.html
3. The Mozilla Project, http://www.mozilla.org
4. SpamArchive. http://www.spamarchive.org
5. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian approach to filtering junk email. AAAI Workshop on Learning for Text Categorization, Jul. 1998, Madison, Wisconsin. AAAI Technical Report WS-98-05
6. I. Androutsopoulos, J. Koutsias, K. V. Chandrinos, G. Paliouras, and C. D. Spyropoulos. An Evaluation of Naive Bayesian AntiSpam Filtering. In Proc. of the workshop on Machine Learning in the New Information Age, 2000.
7. A. Kolcz and J. Alspector. SVM-based filtering of e-mail spam with content-specific misclassification costs. ICDM Workshop on Text Mining (TextDM 2001), Nov. 2001.
8. X. Carreras and L. Mrquez. Boosting trees for anti-spam email filtering. In Proceedings of RANLP-01, 4th International Conference on Recent Advances in Natural Language Processing, Tzigov Chark, BG, 2001
9. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
10. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
11. B. Scolkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Technical Report 99-87, Microsoft Research, 1999.
12. C.-T. Wu, et al. A Novel Embedded-Text-in-Image Detector and Its Applications. UCSB Technical Report, January 2005.
13. L.A. Fletcher and R. Kasturi. A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 910-918, Nov. 1988
14. R. Lienhart and A. Wernicked. Localizing and segmenting text in images and videos. IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 236-268, Apr. 2002.
15. D. Chen, H. Bourlard, and J-Ph. Thiran. Text identification in complex background using SVM. In Proc. IEEE CVPR, p.621-626, Dec. 2001
16. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001.