An Adaptive Approach for Content Based Image Retrieval Using Gaussian Firefly Algorithm

  • T. Kanimozhi
  • K. Latha
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


An adaptive content based image retrieval (CBIR) approach based on relevance feedback and Gaussian Firefly algorithm is proposed in this paper. Feature extraction has been done with the Euclidean distance estimation between the pixels; relevance feedback (RF) based approach but all concerns with the extraction of image accuracy. This research work has a focused approach to increase the performance by optimizing image feature by adopting with the firefly algorithm (FA). Further, to improve the retrieval accuracy, random walk concepts based on Gaussian distribution is used to move all the fireflies to global best at the end of each iteration. Experiments demonstrate that the proposed method shows more accuracy and better performance compared to particle swarm optimization and genetic algorithm and the use of Gaussian distribution further improve the retrieval accuracy.


Content-based image retrieval Relevance Feedback Firefly Algorithm color descriptor texture descriptor Gaussian distribution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Smeulders, A.W., …Jain, R.: Content- based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)Google Scholar
  2. 2.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2) (2008)Google Scholar
  3. 3.
    Grigorova, A., …Huang, T.S.: Content based image retrieval by feature adaptation and relevance feedback. IEEE Trans. Multimedia 9(6), 1183–1192 (2007)Google Scholar
  4. 4.
    Wu, Y., Zhang, A.: A feature re-weighing approach for relevance feedback in image retrieval. In: Proc. IEEE Int. Conf. Image Processing (ICIP 2002), vol. 2, pp. 581–584 (2002)Google Scholar
  5. 5.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Farahani, S.M., …Meybodi, M.R.: A Gaussian Firefly Algorithm. International Journal of Machine Learning and Computing 1(5) (December 2011)Google Scholar
  7. 7.
    Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: An experimental comparison. Inf. Retriev. 11(2), 77–107 (2008)CrossRefGoogle Scholar
  8. 8.
    Broilo, M., De Natale, F.G.B.: A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization. IEEE Trans. Multimedia 12(4) (June 2010)Google Scholar
  9. 9.
    Yazdani, D., Meybodi, M.R.: AFSA-LA: A New Model for Optimization. In: Proceedings of the 15th Annual CSI Computer Conference (CSICC 2010), February 20-22 (2010)Google Scholar
  10. 10.
    The corel database for content based image retrieval,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • T. Kanimozhi
    • 1
  • K. Latha
    • 1
  1. 1.Dept. of Computer Science & Engg, BIT CampusAnna UniversityTiruchirappalliIndia

Personalised recommendations