An Online Content Based Email Attachments Retrieval System

https://doi.org/10.24017/science.2017.1.12

Abstract views: 1068 / PDF downloads: 1662

Authors

  • Noor Ghazi M Jameel Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
  • Esraa Zeki Mohammed Kirkuk Dept, State company for Internet Services, Kirkuk, Iraq
  • Loay Edwar George Computer Science Dept, University of Baghdad, Baghdad, Iraq

Abstract

E-mail is one of the most popular programs used by most people today. As a result of the continuous daily use, thousands of messages are accumulated in the electronic box of most individuals, which make it difficult for them after a period of time to retrieve the attachments of these messages. Most Email providers constantly improved their search technology, but till now there is something could not be done; i.e., searching inside attachments. Some email providers like Gmail has added searching words inside attachments for some file types (.pdf files, .doc documents, .ppt presentations) but for image files this feature not supported till now. However, E-mail providers and even modern researchers have not focused on retrieving the image attachments in the E- mail box. The paper was aimed to introduce a novel idea of using Content based Image Retrieval (CBIR) in E-mail application to retrieve images from email attachments based on entire contents. The work main phases are: feature extraction based on color features and connect to Email server to read Emails, the second phase is retrieving similar image attachments. The tests carried on email inbox contain 100 messages with 500 image attachments and gave good precision and recall rates When the threshold value is less than or equal to 0.4.

Keywords:

CBIR, Color Features, Email Attachments, Email Retrieval System, Image Retrieval, Similarity Measure

References

1. X. Qian, X. Tan, Y. Zhang, R. Hong, and M. Wang, "Enhancing sketch-based image retrieval by re-ranking and relevance feedback", IEEE Trans. Image Processing, Vol. 25, pp. 195-208, 2015.
https://doi.org/10.1109/TIP.2015.2497145
2. M. Azodinia, and A. Hajdu, "A Novel combinational relevance feedback based method for content-based image retrieval", ActaPolytechnicaHungarica, Vol. 13, no. 5, pp. 121-134, 2016.
https://doi.org/10.12700/APH.13.5.2016.5.7
3. A. Saini and R. Bharti "A review on content based image retrieval by different techniques", International Journal of Neural Systems Engineering, Vol. 1, no. 1, pp. 1-6, 2017
https://doi.org/10.21742/ijnse.2017.1.1.01
4. S. B. Pitla, "Organizational Search in Email Systems", M.S. thesis, Dept. Mathematics and Computer Science, Western Kentucky Univ., 2012.
5. L. E. George, and E. Z. Mohammed, "Tissues image retrieval system based on Co-occurrence, run length and roughness features", IEEE Conference Publications, International Conference on Computer Medical Applications (ICCMA), DOI: 10.1109/ICCMA.2013.6506186, pp. 1-6, 2013.
https://doi.org/10.1109/ICCMA.2013.6506186
6. I. Alsmadi, and I. Alhami, "Clustering and classification of email contents", Journal of King Saud University - Computer and Information Sciences, Production and hosting by Elsevier B.V. on behalf of King Saud University, Vol. 27, pp. 46-57, 2015.
https://doi.org/10.1016/j.jksuci.2014.03.014
7. D. Yuvaraj, and S. Hariharan, "Content-based image retrieval based on integrating region segmentation and colour histogram", International Arab Journal of Information Technology, Vol. 13, pp. 203-207, 2016.
8. S. R. Dubey, S. K. Singh, and R. K. Singh, "Multichannel decoded local binary patterns for content based image retrieval", IEEE Trans. Image Processing, Vol. 25, pp. 4018-4032, 2016.
https://doi.org/10.1109/TIP.2016.2577887
9. J. PyykkÖ and D. Glowacka, "Interactive content-based image retrieval with deep neural networks", Symbiotic 2016, LNCS 9961, pp. 77-88, 2017
https://doi.org/10.1007/978-3-319-57753-1_7
10. Parthiban S. and Srinivasa Raghavan S., "Content based image classification and retrieval using visual bag of features and adaboost algorithm", ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 2, pp. 588-590, 2017.
11. J. F. Kurose, and K. W. Ross, "Application layer in Computer Networking a Top-Down Approach", 6th ed., USA: Pearson Education, Inc., pp. 118-130, 2013.
12. A. S. Tanenbaum, and D. J. Wetherall, "The application layer in Computer Networks", 5th ed., USA: Pearson Education, Inc., pp. 623-646, 2011.
13. L. L. Peterson and B. S. Davie, "Application in Computer Networks a systems approach", 5th ed., USA: Elsevier, Inc., pp. 700-708, 2012.
14. B. A. Forouzan, "Remote logging, electronic mail, and file transfer" in "Data Communications and Networking", 4th ed., USA: McGraw-Hill, pp. 824-840, 2007.
15. T. Kato, "Database Architecture for Content-Based Image Retrieval", Proceedings of Image Storage and Retrieval Systems (SPIE), pp. 112-123, 1992.
https://doi.org/10.1117/12.58497
16. J. Eakins, and M.Graham, "Content-based image retrieval", University of Northumbria at Newcastle, Report no. 39, 1999.
17. E. Aulia, "Hierarch Indexing for Region Based Image Retrieval", M.Sc. Thesis, Department of Industrial and Manufacturing Systems Engineering, Louisiana State University, 2001.
18. J. Huang, "Color-Spatial Image Indexing and Applications", Ph.D. Thesis, Cornell University, 1998.
19. C., Li Wei, C., and R.Wilson, "A general framework for content-based medical image retrieval with its application to Mammograms", Proceedings of the SPIE, Vol. 5748, pp. 134-143, 2005.
20. G.Brunner, "Structure features for content-based image retrieval and classification problems", Ph.D. Thesis, University of Freiburg, Germany, 2006.
https://doi.org/10.1007/11550518_53

Downloads

How to Cite

[1]
N. G. M Jameel, E. Z. Mohammed, and L. E. George, “An Online Content Based Email Attachments Retrieval System”, KJAR, vol. 2, no. 1, pp. 68–73, Jun. 2017, doi: 10.24017/science.2017.1.12.

Article Metrics

Published

30-06-2017

Issue

Section

Pure and Applied Science