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Content-based blur image retrieval using quaternion approach and frequency adder LBP

  • Komal Nain Sukhia
  • M. Mohsin Riaz
  • Abdul GhafoorEmail author
Article
  • 9 Downloads

Abstract

The paper presents a content based image retrieval scheme based on feature extraction and weighing. Features are extracted using frequency adder based local binary pattern and blur detection metric which are then optimally combined using a weighing scheme. Simulations are performed on modified Wang and KTH-TIPS databases, which include images from four different classes of blur respectively. Comparison of simulation results with the state-of-the-art techniques show better retrieval precision and recall values for proposed technique.

Keywords

Content based image retrieval Quaternion Frequency adder local binary pattern Blur detection 

Notes

References

  1. 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.CrossRefGoogle Scholar
  2. Alzubi, A., Amira, A., & Ramzan, N. (2017). Content-based image retrieval with compact deep convolutional features. Neurocomputing, 249, 95–105.CrossRefGoogle Scholar
  3. Boomilingam, T., & Subramaniam, M. (2017). An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network. Multimedia Tools and Applications, 76(20), 21729–21747.CrossRefGoogle Scholar
  4. Corel photo collection color image database. http://wang.ist.psu.edu/docs/realted/.
  5. Denis, P., Carre, P., & Fernandez-Maloigne, C. (2007). Spatial and spectral quaternionic approaches for colour images. Computer Vision and Image Understanding, 107(1–2), 74–87.CrossRefGoogle Scholar
  6. Dubey, S. R., Singh, S. K., & Singh, R. K. (2016). Multichannel decoded local binary patterns for content-based image retrieval. IEEE Transactions on Image Processing, 25(9), 4018–4032.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Dubey, S. R., Singh, S. K., & Singh, R. K. (2017). Local SVD based NIR face retrieval. Journal of Visual Communication and Image Representation, 49, 141–152.CrossRefGoogle Scholar
  8. Ell, T. A., & Sangwine, S. J. (2007). Hypercomplex Fourier transforms of color images. IEEE Transactions on Image Processing, 16(1), 22–35.MathSciNetCrossRefzbMATHGoogle Scholar
  9. Fadaei, S., Amirfattahi, R., & Ahmadzadeh, M. R. (2017). New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Processing, 11(2), 89–98.CrossRefGoogle Scholar
  10. Giveki, D., Soltanshahi, M. A., & Montazer, G. A. (2017). A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. International Journal for Light and Electron Optics, 131, 242–254.CrossRefGoogle Scholar
  11. Goncalves, F. M. F., Guilherme, I. R., & Pedronette, D. C. G. (2017). Semantic guided interactive image retrieval for plant identification. Expert Systems with Applications, 91, 12–26.CrossRefGoogle Scholar
  12. Hamilton, W. R. (1866). Elements of quaternions. Longmans: Green, & Company.Google Scholar
  13. Karakasis, E. G., Papakostas, G. G., Koulouriotis, D. E., & Tourassis, V. D. (2014). A unified methodology for computing accurate quaternion color moments and moment invariants. IEEE Transactions on Image Processing, 23(2), 596–611.MathSciNetCrossRefzbMATHGoogle Scholar
  14. Khokher, A., & Talwar, R. (2017). A fast and effective image retrieval scheme using color, texture, and shape-based histograms. Multimedia Tools and Applications, 76(20), 21787–21809.CrossRefGoogle Scholar
  15. Kundu, M. K., Chowdhury, M., & Bulo, S. R. (2015). A graph-based relevance feedback mechanism in content-based image retrieval. Knowledge-Based Systems, 73, 254–264.CrossRefGoogle Scholar
  16. Lan, R., & Zhou, Y. (2017). Medical image retrieval via histogram of compressed scattering coefficients. IEEE Journal of Biomedical and Health Informatics, 21(5), 1338–1346.MathSciNetCrossRefGoogle Scholar
  17. Lan, R., Zhou, Y., & Tang, Y. Y. (2017). Quaternionic weber local descriptor of color images. IEEE Transactions on Circuits and Systems for Video Technology, 27(2), 261–274.CrossRefGoogle Scholar
  18. Liu, P., Guo, J. M., Wu, C. Y., & Cai, D. (2017). Fusion of deep learning and compressed domain features for content based image retrieval. IEEE Transactions on Image Processing, 99, 1–1.MathSciNetzbMATHGoogle Scholar
  19. Moxey, C. E., Sangwine, S. J., & Ell, T. A. (2003). Hypercomplex correlation techniques for vector images. IEEE Transactions on Signal Processing, 51(7), 1941–1953.MathSciNetCrossRefzbMATHGoogle Scholar
  20. Narvekar, N. D., & Karam, L. J. (2011). A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing, 20(9), 2678–2683.MathSciNetCrossRefzbMATHGoogle Scholar
  21. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.CrossRefzbMATHGoogle Scholar
  22. Paul, S., & Das, S. (2015). Simultaneous feature selection and weighting—An evolutionary multi-objective optimization approach. Pattern Recognition Letters, 65, 51–59.CrossRefGoogle Scholar
  23. Paul, T. K., & Ogunfunmi, T. (2015). A kernel adaptive algorithm for quaternion-valued inputs. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2422–2439.MathSciNetCrossRefGoogle Scholar
  24. Pavithra, L. K., & Sharmila, T. S. (2017). An efficient framework for image retrieval using color, texture and edge features. Computers and Electrical Engineering, 70, 1–14.Google Scholar
  25. Pei, S. C., & Cheng, C. M. (1999). Color image processing by using binary quaternion-moment-preserving thresholding technique. IEEE Transactions on Image Processing, 8(5), 614–628.CrossRefGoogle Scholar
  26. Sangwine, S. J. (1996). Fourier transforms of colour images using quaternion or hypercomplex, numbers. Electronics Letters, 32(21), 1979–1980.CrossRefGoogle Scholar
  27. Shrivastava, N., & Tyagi, V. (2016). An integrated approach for image retrieval using local binary pattern. Multimedia Tools and Applications, 75(11), 6569–6583.CrossRefGoogle Scholar
  28. Srivastava, P., & Khare, A. (2017). Utilizing multiscale local binary pattern for content-based image retrieval. Multimedia Tools and Applications, 77, 1–27.Google Scholar
  29. Srivastava, P., & Khare, A. (2017). Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval. Journal of Visual Communication and Image Representation, 42, 78–103.CrossRefGoogle Scholar
  30. Tang, X., Jiao, L., & Emery, W. J. (2017). SAR image content retrieval based on fuzzy similarity and relevance feedback. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1824–1842.CrossRefGoogle Scholar
  31. Wu, J., Feng, L., Liu, S., & Sun, M. (2017). Image retrieval framework based on texton uniform descriptor and modified manifold ranking. Journal of Visual Communication and Image Representation, 49, 78–88.CrossRefGoogle Scholar
  32. Zhang, D., Tang, J., Jin, G., Zhang, Y., & Tian, Q. (2017). Region similarity arrangement for large-scale image retrieval. Neurocomputing, 272, 461–470.CrossRefGoogle Scholar
  33. Zhu, H., & Xie, Q. (2018). Content-based image retrieval using student’s t-mixture model and constrained multiview nonnegative matrix factorization. Multimedia Tools and Applications, 77(11), 14207–14239.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.National University of Sciences and TechnologyIslamabadPakistan
  2. 2.COMSATS UniversityIslamabadPakistan

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