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Object-Based Image Retrieval System Using Rough Set Approach

  • Neveen I. Ghali
  • Wafaa G. Abd-Elmonim
  • Aboul Ella Hassanien
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)

Abstract

In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Preprocessing and Object-based image retrieval. In preprocessing, an imagebased object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval system is demonstrated. Experimental results show that the proposed system performs well in terms of speed and accuracy.

Keywords

Image Retrieval Image Database Query Image Granular Computing Image Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Amato, A., Lecce, V.D.: A knowledge based approach for a fast image retrieval system. Image and Vision Computing 26, 1466–1480 (2008)CrossRefGoogle Scholar
  2. 2.
    Hirano, S., Tsumoto, S.: Rough representation of a region of interest in medical images. Int. J. of Approximate Reasoning 40, 23–34 (2005)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Li, J., Wang, J.Z.: Machine learning and statistical modeling approaches to image retrieval. Kluwer Academic Publishers (2004)Google Scholar
  4. 4.
    Deb, S.: Multimedia Systems and Content-Based Image Retrieval. Idea Group Publishing (2004)Google Scholar
  5. 5.
    Dong-cheng, S., Lan, X., Ling-yan, H.: Image Retrieval using both Color and Texture Features. The Journal of China Universities of Posts and Telecommunications 14(1), 94–99 (2007)Google Scholar
  6. 6.
    Goodrum, A.A.: Image Information Retrieval: An overview of current research. Informing Science, Special Issue on Information Science Research 3(2), 63–67 (2000)Google Scholar
  7. 7.
    Wan-Ting, S., Ju-Chin, C., Jenn-Jier, L.: Region-based image retrieval system with heuristic pre-clustering relevance feedback. Expert Systems with Applications 37(7), 4984–4998 (2010)CrossRefGoogle Scholar
  8. 8.
    Liu, Y., Zhang, D., Lu, G., Ma, W.: Asurvey of content-based image retrieval with high-level semantics. Pattern Recognition 40(1), 262–282 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A Review of content-based image retrieval systems in medical applications-clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2004)CrossRefGoogle Scholar
  10. 10.
    Shankar, P.K., Uma, S.B., Mitra, P.: Granular Computing, Rough Entropy and Object Extraction. Pattern Recognition Letters 26, 2509–2517 (2005)CrossRefGoogle Scholar
  11. 11.
    Pawlak, Z.: On Rough Sets. Bulletin of the European Association for Theoretical Computer Science (24), 94–109 (1984)Google Scholar
  12. 12.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Scieviolnces 11(5) (1982)Google Scholar
  13. 13.
    Pawlak, Z.: Some Remarks about Rough Sets. In: ICS PAS, vol. 456 (1982)Google Scholar
  14. 14.
    Wang, Y., Ding, M., Zhou, C., Hu, Y.: Interactive relevance feedback mechanism for image retrieval using rough set. Knowledge-Based Systems 19(8), 696–703 (2006)CrossRefGoogle Scholar
  15. 15.
    Yan, G.: Pixel Based and Object Oriented Image Analysis for Coal Fire Research, Master thesis, Enschede, The Netherlands, International Institute for Geoinformation Science and Earth Observation (ITC), Netherlands (2003)Google Scholar
  16. 16.
    Im, Y.H., Oh, S.G., Chung, M.J., Yu, J.H., Lee, H.S., Chang, J.K., Park, D.H.: A KFD web database system with an object-based image retrieval for family art therapy assessments. The Arts in Psychotherapy 37(3), 163–171 (2010)CrossRefGoogle Scholar
  17. 17.
    Alajlan, N., Kamel, M., Freeman, G.: Multi-object image retrieval based on shape and topology. Signal Processing: Image Communication 21(10), 904–918 (2006)CrossRefGoogle Scholar
  18. 18.
    Shao, L., Brady, M.: Specific object retrieval based on salient regions. Pattern Recognition 39(10), 1932–1948 (2006)CrossRefzbMATHGoogle Scholar
  19. 19.
    Liu, D., Chen, T.: Video retrieval based on object discovery. Computer Vision and Image Understanding 113(3), 397–404 (2009)CrossRefGoogle Scholar
  20. 20.
    Xie, Z., Roberts, C., Johnson, B.: Object-based target search using remotely sensed data: A case study in detecting invasive exotic Australian Pine in South Florida. ISPRS Journal of Photogrammetry and Remote Sensing 63(6), 647–660 (2008)CrossRefGoogle Scholar
  21. 21.
    Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld: A system for region-based image indexing and retrieval. In: Third International Conference on Visual Information Systems, Amsterdam, Netherland, pp. 509–516 (1999)Google Scholar
  22. 22.
    Graham, M.E.: Enhancing visual resources for searching and retrieval-Is content based image retrieval solution? Literary and Linguistic Computing 19(3), 321–333 (2004)CrossRefGoogle Scholar
  23. 23.
    Viitaniemi, V., Laaksonen, J.: Techniques for Still Image Scene Classification and Object Detection. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 35–44. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Abraham, A., Hassanien, A., Carvalho, A.: Foundations of Computational Intelligence: Bio-Inspired Data Mining. Studies in Computational Intelligence, vol. 4. Springer, Germany (2009) ISBN: 978-3-642-01087-3Google Scholar
  25. 25.
    Hassanien, A.: Intelligent techniques for prostate ultrasound image analysis. Int. Jour. of Hybrid Intelligent Systems 6, 155–167 (2009), doi:10.3233/HIS-2009-0092Google Scholar
  26. 26.
    Hassanien, A., Abraham, A.: Rough morphology hybrid approach for mammography image classification and prediction. Int. Jour. of Computational Intelligence and Applications 7(1), 17–42 (2008)CrossRefGoogle Scholar
  27. 27.
    Hassanien, A.: Pulse coupled neural network for detection of masses in digital mammogram. Neural Network World Journal 2(6), 129–141 (2006)Google Scholar
  28. 28.
    Mohabey, A., Ray, A.K.: Rough set theory based segmentation of color images. In: Proc. of NAFIPS 19th Int. Conf., pp. 338–342 (2000)Google Scholar
  29. 29.
    Sinha, D., Laplante, P.: A rough set-based approach to handling spatial uncertainty in binary images. Eng. Appl. Artif. Intell. 17, 97–110 (2004)CrossRefGoogle Scholar
  30. 30.
    Pal, S.K., Shankar, U., Mitra, P.: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16), 2509–2517 (2005)CrossRefGoogle Scholar
  31. 31.
    Hassanien, A., Abraham, A., Peters, J.F., Schaefer, G., Henry, C.: Rough sets and near sets in medical imaging: A review. IEEE Trans. Info. Tech. in Biomedicine (2009), doi:10.1109/TITB.2009.2017017Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Neveen I. Ghali
    • 1
  • Wafaa G. Abd-Elmonim
    • 1
  • Aboul Ella Hassanien
    • 2
  1. 1.Faculty of ScienceAl-Azhar UniversityCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt

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