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Interactive Biogeography Particle Swarm Optimization for Content Based Image Retrieval

  • Deepika Dubey
  • Geetam Singh TomarEmail author
Article
  • 3 Downloads

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.

Keywords

Content based image retrieval (CBIR) Color histogram ANN Edge detection BPSO Image retrieval 

Notes

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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).Google Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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).Google Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 12.
    Ho, H. T., & Chellappa, R. (2013). Pose-invariant face recognition using Markov random fields. IEEE Transactions on Image Processing, 22(4), 1573–1584.MathSciNetCrossRefzbMATHGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  18. 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. 19.
    Jenni, K., Mandala, S., & Sunar, M. S. (2015). Content based image retrieval using colour strings comparison. Procedia Computer Science, 50, 374–379.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefzbMATHGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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. 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).Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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).Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. 29.
    Gudivada, V. N., & Raghavan, V. V. (1997). Modeling and retrieving images by content. Information Processing and Management, 33(4), 427–452.CrossRefGoogle Scholar
  30. 30.
    Hill, T., O’Connor, M., & Remus, W. (1996). Neural networks models for time series forecasts. Management Sciences, 42(7), 1082–1092.CrossRefzbMATHGoogle Scholar
  31. 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).Google Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.MathSciNetCrossRefGoogle Scholar
  34. 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.MathSciNetCrossRefGoogle Scholar
  35. 35.
    Yan, W. (2012). Toward automatic time-series forecasting using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 23(7), 1028–1039.CrossRefGoogle Scholar
  36. 36.
    Trelea, I. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85, 317–325.MathSciNetCrossRefzbMATHGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 38.
    Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE international conference on neural networks (ICNN), Piscataway, NJ (pp. 1942–1948).Google Scholar
  39. 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).Google Scholar
  40. 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.CrossRefGoogle Scholar
  41. 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).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUttarakhand Technical UniversityDehradunIndia
  2. 2.Department of Computer Science and EngineeringTHDC-IHETTehriIndia

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