Skip to main content
SpringerLink
Log in

Interactive Biogeography Particle Swarm Optimization for Content Based Image Retrieval

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, we propose biogeography particle swarm optimization (BPSO) approach for content based image retrieval using low-level features like color and texture. This approach is based on the content used in the image and its classification. The above-mentioned BPSO is inspired by migrated species biogeographically. The proposed approach is based on algorithms like color, histogram and texture which are applicable on color images. The use of these algorithms ensures that the suggested image retrieval approach fabricates such results which are highly applicable to the content of an image query. Features such as distance measure, color and histogram are used to find out color features of an image and filters are used to extract the texture feature. Well know precision and recall measures are used and the output results are compared with other recently used related approaches. Our proposed approach is having better performance as compared to past approaches as it uses features based on color and texture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Zand, M., Doraisamy, S., Halin, A. A., & Mustaffa, M. R. (2015). Texture classification and discrimination for region-based image retrieval. Journal of Visual Communication and Image Representation, 26, 305–316.

    Article  Google Scholar 

  2. Raji, R., Mishra, D., & Nair, M. S. (2015). A novel texture based automated histogram specification for color image enhancement using image fusion. Procedia Computer Science, 46, 1501–1509.

    Article  Google Scholar 

  3. Patil, J. K., & Kumar, R. (2016). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10(2), 69–78. https://doi.org/10.1016/j.eaef.2016.11.004.

    Article  Google Scholar 

  4. Rahmani, M. K. I., & Ansari, M. A. (2013). A color based fuzzy algorithm for CBIR. In Confluence 2013: The next generation information technology summit (4th international conference), Noida (pp. 363–370).

  5. Alsmadi, M. K. (2017). An efficient similarity measure for content based image retrieval using memetic algorithm. Egyptian Journal of Basic and Applied Sciences, 4(2), 112–122. https://doi.org/10.1016/j.ejbas.2017.02.004.

    Article  Google Scholar 

  6. Hussain, C. A., Venkata Rao, D., & Aruna Masthani, S. (2016). Robust pre-processing technique based on saliency detection for content based image retrieval systems. Procedia Computer Science, 85, 571–580.

    Article  Google Scholar 

  7. Kaipravan, M., 7 Rejiram, R. (2016). A novel CBIR system based on combination of color moment and Gabor filter. In 2016 international conference on data mining and advanced computing (SAPIENCE), Ernakulam (pp. 170–174).

  8. Liu, P., Guo, J.-M., Chamnongthai, K., & Prasetyo, H. (2017). Fusion of color histogram and LBP-based features for texture image retrieval and classification. Information Sciences, 390, 95–111.

    Article  Google Scholar 

  9. Liu, P., Guo, J.-M., Chamnongthai, K., & Prasetyo, H. (2017). Fusion of color histogram and LBP-based features for texture image retrieval and classification. Information Sciences, 390, 95–111.

    Article  Google Scholar 

  10. Mazharul Islam, S., Banerjee, M., Bhattacharyya, S., & Chakraborty, S. (2017). Content-based image retrieval based on multiple extended fuzzy-rough framework. Applied Soft Computing, 57, 102–117.

    Article  Google Scholar 

  11. Smeets, D., Claes, P., Hermans, J., Vandermeulen, D., & Suetens, P. (2012). A comparative study of 3-D face recognition under expression variations. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5), 710–727.

    Article  Google Scholar 

  12. Ho, H. T., & Chellappa, R. (2013). Pose-invariant face recognition using Markov random fields. IEEE Transactions on Image Processing, 22(4), 1573–1584.

    Article  MathSciNet  MATH  Google Scholar 

  13. Yong, X., Fang, X., Li, X., Yang, J., You, J., Liu, H., et al. (2014). Data uncertainty in face recognition. IEEE Transactions on Cybernetics, 44(10), 1950–1961.

    Article  Google Scholar 

  14. De Marsico, M., Nappi, M., Riccio, D., & Wechsler, H. (2013). Robust face recognition for uncontrolled pose and illumination changes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(1), 149–163.

    Article  Google Scholar 

  15. Lee, P. H., Hsu, G. S., Wang, Y. W., & Hung, Y. P. (2012). Subject-specific and pose-oriented facial features for face recognition across poses. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(5), 1357–1368.

    Article  Google Scholar 

  16. Li, S., Liu, X., Chai, X., Zhang, H., Lao, S., & Shan, S. (2014). Maximal likelihood correspondence estimation for face recognition across pose. IEEE Transactions on Image Processing, 23(10), 4587–4600.

    Article  MathSciNet  MATH  Google Scholar 

  17. Li, D., Zhou, H., & Lam, K. M. (2015). High-resolution face verification using pore-scale facial features. IEEE Transactions on Image Processing, 24(8), 2317–2327.

    Article  MathSciNet  MATH  Google Scholar 

  18. Tiwari, A. K., Kanhangad, V., & Pachori, R. B. (2017). Histogram refinement for texture descriptor based image retrieval. Signal Processing: Image Communication, 53, 73–85.

    Google Scholar 

  19. Jenni, K., Mandala, S., & Sunar, M. S. (2015). Content based image retrieval using colour strings comparison. Procedia Computer Science, 50, 374–379.

    Article  Google Scholar 

  20. Ratyal, N. I., Taj, I. A., Bajwa, U. I., & Sajid, M. (2015). 3D face recognition based on pose and expression invariant alignment. Computers & Electrical Engineering, 46, 241–255.

    Article  Google Scholar 

  21. Alzu’bi, A., Amira, A., & Ramzan, N. (2015). Semantic content-based image retrieval: A comprehensive study. Journal of Visual Communication and Image Representation, 32, 20–54.

    Article  Google Scholar 

  22. Andaló, F. A., Miranda, P. A. V., da S. Torres, R., & Falcão, A. X. (2010). Shape feature extraction and description based on tensor scale. Pattern Recognition, 43(1), 26–36.

    Article  MATH  Google Scholar 

  23. Celik, C., & Bilge, H. S. (2017). Content based image retrieval with sparse representations and local feature descriptors: A comparative study. Pattern Recognition, 68, 1–13.

    Article  Google Scholar 

  24. Hanmandlu, M., Verma, O. P., Susan, S., & Madasu, V. K. (2013). Color segmentation by fuzzy co-clustering of chrominance color features. Neuro Computing, 120, 235–249.

    Google Scholar 

  25. Jain, R., & Johari, P. K. (2016). An improved approach of CBIR using Color based HSV quantization and shape based edge detection algorithm. In 2016 IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), Bangalore (pp. 1970–1975).

  26. Abuhaiba, Ibrahim S. I., & Salamah, Ruba A. A. (2012). Efficient Global and Region Content Based Image Retrieval. I.J. Image, Graphics and Signal Processing, 2012(5), 38–46.

    Article  Google Scholar 

  27. Jamil, N., Lqbal, S., & Iqbal, N. (2001). Face recognition using neural networks. In Proceedings. IEEE international multi topic conference, IEEE INMIC 2001. Technology for the 21st century (pp. 277–281).

  28. Missaoui, R., Sarifuddin, M., & Vaillancourt, J. (2005). Similarity measures for efficient content-based image retrieval. IEEE Proceedings - Vision, Image and Signal Processing, 152(6), 875–887.

    Article  Google Scholar 

  29. Gudivada, V. N., & Raghavan, V. V. (1997). Modeling and retrieving images by content. Information Processing and Management, 33(4), 427–452.

    Article  Google Scholar 

  30. Hill, T., O’Connor, M., & Remus, W. (1996). Neural networks models for time series forecasts. Management Sciences, 42(7), 1082–1092.

    Article  MATH  Google Scholar 

  31. Adhikari, R., & Agrawal, R. K. (2011). Effectiveness of PSO based neural network for seasonal time series forecasting. In Indian international conference on artificial intelligence (IICAI), Tumkur, India (pp. 232–244).

  32. Wang, X.-Y., Yong-Jian, Yu., & Yang, H.-Y. (2011). An effective image retrieval scheme using color, texture and shape features. Computer Standards & Interfaces, 33(1), 59–68.

    Article  Google Scholar 

  33. Wang, X.-Y., Yang, H.-Y., & Li, D.-M. (2013). A new content-based image retrieval technique using color and texture information. Computers & Electrical Engineering, 39(3), 746–761.

    Article  MathSciNet  Google Scholar 

  34. Zhang, P., Wei, P., & Yu, H. Y. (2012). Biogeography-based optimization search algorithm for block matching motion estimation. IET Image Processing, 6(7), 1014–1023.

    Article  MathSciNet  Google Scholar 

  35. Yan, W. (2012). Toward automatic time-series forecasting using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 23(7), 1028–1039.

    Article  Google Scholar 

  36. Trelea, I. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85, 317–325.

    Article  MathSciNet  MATH  Google Scholar 

  37. Clerc, M., & Kennedy, J. (2002). The particle swarm—Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73.

    Article  Google Scholar 

  38. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE international conference on neural networks (ICNN), Piscataway, NJ (pp. 1942–1948).

  39. Chen, A. P., Huang, C. H., & Hsu, Y. C. (2011). Particle swarm optimization with inertia weight and constriction factor. In International conference on swarm intelligence (ICSI), Cergy, France (pp. 1–11).

  40. Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35–62.

    Article  Google Scholar 

  41. Jha, G. K., Thulasiraman, P., & Thulasiram, R. K. (2009). PSO based neural network for time series forecasting. In IEEE international joint conference on neural networks (IJCNN), Atlanta, Georgia, USA, June 14–19 (pp. 1422–1427).

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun, 248007, India

    Deepika Dubey

  2. Department of Computer Science and Engineering, THDC-IHET, Tehri, 249124, India

    Geetam Singh Tomar

Authors
  1. Deepika Dubey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geetam Singh Tomar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Geetam Singh Tomar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dubey, D., Tomar, G.S. Interactive Biogeography Particle Swarm Optimization for Content Based Image Retrieval. Wireless Pers Commun 107, 907–921 (2019). https://doi.org/10.1007/s11277-019-06308-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06308-y

Keywords

Search

Navigation