Comparison of query optimization techniques in content based image retrieval (CBIR)

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    With increasing the popularity of World Wide Web, storing digital contents increases enormously, in that case, it is important to implement convenient information systems which manage the collections of these digital contents efficiently. This paper concentrates on hastening techniques for efficient retrieval of images. Content-Based Image Retrieval (CBIR) systems are used by common approaches. These systems support retrieving similar images depend on content properties (e.g., color, shape, and texture) by retrieving automatically similar images to a pattern or user-defined specification. The CBIR generally used in several applications by applying different techniques in each application which in turns enhance the retrieval process. The paper aims to evaluate some of these applications and compare them to find out the proper methods that return the best results in these CBIR systems.

  • Keywords

    Content-Based Image Retrieval; CBIR; Query Optimization; A Multimedia Database; Image Retrieval.

  • References

      [1] (2017). Multimedia database. [online] Available at: [Accessed 14 Mar. 2017].

      [2] (2017). Multimedia Database. [online] Available at: [Accessed 19 Mar. 2017].

      [3] Negoiţă, C., & Vlădoiu, M. Querying and Information Retrieval in Multimedia Databases. Universității Petrol–Gaze Din Ploieşti 2006th ser. VIII, 2.‏

      [4] Andrade, H., Kurc, T., Sussman, A. and Saltz, J. (2004). Optimizing the execution of multiple data analysis queries on parallel and distributed environments. IEEE Transactions on Parallel and Distributed Systems, 15(6), pp.520-532.

      [5] Sadat, A. and Lecca, P. (2009). On the Performances in Simulation of Parallel Databases: An Overview on the Most Recent Techniques for Query Optimization. 2009 International Workshop on High Performance Computational Systems Biology.

      [6] Chan, S., Qing Li, Yi Wu, and Yueting Zhuang, (2002). Accommodating hybrid retrieval in a comprehensive video database management system. IEEE Transactions on Multimedia, 4(2), pp.146-159.

      [7] Ruziana Mohamad Rasli, Su-Cheng Haw, and Chee-Onn Wong, (2010). A survey on optimizing video and audio query retrieval in multimedia databases. 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

      [8] Zhou, W., Dao, S. and Jay Kuo, C. (2002). On-line knowledge- and rule-based video classification system for video indexing and dissemination. Information Systems, 27(8), pp.559-586.

      [9] Agrawal, R., Faloutsos, C. and Swami, A. (1993). Efficient similarity search in sequence databases. Foundations of Data Organization and Algorithms, pp.69-84.

      [10] Shenoy, S. and Ozsoyoglu, Z. (1989). Design and implementation of a semantic query optimizer. IEEE Transactions on Knowledge and Data Engineering, 1(3), pp.344-361.

      [11] Janghyun Yoon, and Jayant, N. (n.d.). Relevance feedback for semantics based image retrieval. Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

      [12] Shen, H., Ooi, B. and Zhou, X. (2005). Towards effective indexing for very large video sequence database. Proceedings of the 2005 ACM SIGMOD international conference on Management of data - SIGMOD '05.

      [13] Li, W., Candan, K., Hirata, K. and Hara, Y. (1997). A hybrid approach to multimedia database systems through integration of semantics and media-based search. Lecture Notes in Computer Science, pp.182-197.

      [14] Wichterich, M., Assent, I., Kranen, P. and Seidl, T. (2008). Efficient EMD-based similarity search in multimedia databases via flexible dimensionality reduction. Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD '08.

      [15] Steinacker, A., Ghavam, A. and Steinmetz, R. (2001). Metadata standards for Web-based resources. IEEE Multimedia, 8(1), pp.70-76.

      [16] J. S. Hong, H.Y. Chen, and J. Hsiang. A Digital Museum of Taiwanese Butterflies. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 260–261, San Antonio, Texas, United States, 2000. ACM Press.

      [17] Bin Zhu, M. Ramsey and Hsinchun Chen, "Creating a large-scale content-based airphoto image digital library", IEEE Transactions on Image Processing, vol. 9, no. 1, pp. 163-167, 2000.

      [18] R. Torres, C. Medeiros, M. Gonçcalves and E. Fox, "A digital library framework for biodiversity information systems", International Journal on Digital Libraries, vol. 6, no. 1, pp. 3-17, 2006.

      [19] H. Müller, N. Michoux, D. Bandon and A. Geissbuhler, "A review of content-based image retrieval systems in medical applications—clinical benefits and future directions", International Journal of Medical Informatics, vol. 73, no. 1, pp. 1-23, 2004.

      [20] Shih-Fu Chang, T. Sikora and A. Purl, "Overview of the MPEG-7 standard", IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 688-695, 2001.

      [21] H. Tamura, S. Mori and T. Yamawaki, "Textural Features Corresponding to Visual Perception", IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 6, pp. 460-473, 1978.

      [22] da Silva Torres, R. & Falcão, A. X. (2006). Content-Based Image Retrieval: Theory and Applications.. RITA, 13, 161-185.

      [23] Jia Li, James Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.

      [24] James Z. Wang, Jia Li, Gio Wiederhold, “SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 23, no.9, pp. 947-963, 2001.

      [25] "Project Definition - CBIR",, 2017. [Online]. Available: [Accessed: 19- Mar- 2017].

      [26] "aminert/CBIR", GitHub, 2017. [Online]. Available: [Accessed: 19- Mar- 2017].

      [27] C. Retrieval, "Content Based Image Retrieval - File Exchange - MATLAB Central",, 2017. [Online]. Available: [Accessed: 18- Mar- 2017].




Article ID: 29435
DOI: 10.14419/ijamr.v8i1.29435

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.