Visual-CBIR: Platform for Storage and Effective Manipulation of a Database Images

Kaouther Zekri, Amel Grissa Touzi, Noureddine Ellouze

2015

Abstract

Today, image retrieval system has become a vital necessity for computing users. Different search systems are increasingly invading the computing software markets, such as QBIC, Photobook and BlobWord. The only negative point these systems have in common is their lack of semantics in query processing, low interactivity and the irrelevance of search results. To overcome these limitations, we propose a more efficient alternative system: a system for image retrieval. This new system provides an intelligent search by content, by keyword, by index, etc. To confirm our approach, we have defined a combination with Oracle DBMS that would lead to 1) an advanced modeling of image type using a signature that describes the physical and semantic content of images, 2) the modeling of different types of search by creating stored procedures in PL/SQL language and 3) simple storage and handling of images in database through an intuitive interface. We prove that this system can be used in a distributed environment.

References

  1. Atnafu Besufekad, S., 2003. Modélisation et traitement de requêtes images complexes. Doctoral dissertation, National Institute of Applied Sciences of Lyon, Lyon, 216 pages.
  2. Carson, C., Thomas, M., Belongie, S., et al., 1999. Blobworld: A System for Region-Based Image Indexing and Retrieval. In Visual Information and Information Systems, Third International Conference, VISUAL'99, volume 1614, pages 509-517. Springer.
  3. Daniel, S. Kaster, Pedro, H. Bugatti, Marcelo PoncianoSilva, et al., 2011. MedFMI-SiR: A Powerful DBMS Solution for Large-Scale Medical Image Retrieval. In Information Technology in Bio- and Medical Informatics, Second International Conference, ITBAM 2011, volume 6865, pages 16-30. Springer.
  4. Dimitrovski, I., Guguljanov, P., and Loskovska, S., 2009. Implementation of web-based medical image retrieval system in Oracle. In Adaptive Science & Technology, 2009. ICAST 2009. 2nd International Conference on, pages 192-197. IEEE.
  5. Flickner, M., Sawhney, H., Niblack, W. et al., 1995. Query by image and video content: The qbic system. In Computer, 28(9), 23-32. IEEE.
  6. Gabillaud, J., 2009. Oracle 11g: SQL, PL/SQL, SQL*Plus. France: Editions ENI, 483 pages.
  7. Harald, K., and Paul, M., 2010. Content-Based Image Retrieval Systems - Reviewing and Benchmarking. In Journal of Digital Information Management, volume 8, pages 54-64.
  8. Landré, J., 2005. Analyse multirésolution pour la recherche et l'indexation d'images par le contenu dans les bases de données images - Application à la bases d'images paléontologique Trans'Tyfipal. Doctoral dissertation, University of Burgundy - France, 159 pages.
  9. Lehmann T. M., Güld, M.O., Deselaers, T., et al, 2005. Automatic categorization of medical images for content-based retrieval and data mining. In Computerized Medical Imaging and Graphics, Elsevier, 29(2-3), pages 143-155.
  10. Li, W., Duan, L., Xu, D., and Tsang, I.W., 2011. Textbased image retrieval using progressive multi-instance learning. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2049-2055. IEEE.
  11. Özsu, M. T. and Valduriez, P., 2011. Principles of Distributed Database Systems, Springer-Verlag New York. Third Edition, 845 pages.
  12. Pecenovic, Z., Do, M., Ayer, S., and Vetterli, M., 1998. New methods for image retrieval. In Proceedings of the International Congress on Imaging Science, volume 2, pages 242-246.
  13. Piccard, R. W., Pentland, A., and Sclaroff, S., 1996. Photobook: Content-based manipulation of image databases. In International Journal of Computer Vision, volume 18, pp. 233-254. Springer.
  14. Rod, w., et. al., 2001. Oracle interMedia User's Guide and Reference, Release 9.0.1, Part No. A88786-01, [on line], (30 March 2014) http://docs.oracle.com/html/ A88786_01/title.htm.
  15. Zagoris, K., Chatzichristofis, S. A., Papamarkos, N., and Boutalis, Y.S., 2009. Img(Anaktisi): A Web Content Based Image Retrieval System. In SISAP 7809 Proceedings of the 2009 Second International Workshop on Similarity Search and Application, pages 154-155. IEEE.
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Paper Citation


in Harvard Style

Zekri K., Grissa Touzi A. and Ellouze N. (2015). Visual-CBIR: Platform for Storage and Effective Manipulation of a Database Images . In Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-103-8, pages 81-90. DOI: 10.5220/0005518900810090


in Bibtex Style

@conference{data15,
author={Kaouther Zekri and Amel Grissa Touzi and Noureddine Ellouze},
title={Visual-CBIR: Platform for Storage and Effective Manipulation of a Database Images},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2015},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005518900810090},
isbn={978-989-758-103-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Visual-CBIR: Platform for Storage and Effective Manipulation of a Database Images
SN - 978-989-758-103-8
AU - Zekri K.
AU - Grissa Touzi A.
AU - Ellouze N.
PY - 2015
SP - 81
EP - 90
DO - 10.5220/0005518900810090