Skip to main content
Log in

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this research, for content based image retrieval, an outstanding feature combination based on modulation methods is presented. Including generated improved contourlet transform with spectral correlation coefficient functions features are proposed. These specifications are re-composed as frequency statistic specification, proposing spatial signals dominion. The produced features are Norm-1 values for transformation of two dimensional signals. Since the contourlet transform are more efficient compared to the other transforms, the modified modulation based recombination was used. Finally, modified features was employed for all database images. This paper utilize high pass modulation estimation, for efficient image retrieval high level features. The representations achieved from large-scale database of 10,000 images using the proposed features were applied as textural specification. Recombination of Textural features based on modulated transformation provides robust feature matrix for database image retrieval. This system illustrate higher retrieval percentage up to 90% accuracy in mentioned database. In all classes, proposed method has more than 90% accuracy. The extracted results certify the precision and efficiency of the proposed system. The results were compared with different systems and were discovered to be notable.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Shafei, S., Vahdati, H., Sedghi, T., & Charmin, A. (2021). Novel high level retrieval system based on mathematic algorithm & technique for MRI medical imaging and classification. Journal of Instrumentation. https://doi.org/10.1088/1748-0221/16/07/P07055

    Article  Google Scholar 

  2. Kalami, A., & Sedghi, T. (2019). Database classification of MPEG7 shape using rotation moments and generated stationary transformed features. Journal of Applied Science and Technology. https://doi.org/10.9734/cjast/2019/v34i430136

    Article  Google Scholar 

  3. Gai, S., Wan, M., Wang, L., & Yang, C. (2014). Reduced quaternion matrix for color texture classification. Neural Computing and Applications, 25, 945–954.

    Article  Google Scholar 

  4. Chen, W. (2016). Single-shot imaging without reference wave using binary intensity pattern for optically-secured-based correlation. IEEE Photonics Journal. https://doi.org/10.1109/JPHOT.2016.2523245

    Article  Google Scholar 

  5. Paraiso-Medina, S., Perez-Rey, D., Bucur, A., & Claerhout, B. (2014). Semantic normalization and query abstraction based on SNOMED-CT and HL7: Supporting metacentric clinical trials. IEEE Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2014.2357025

    Article  Google Scholar 

  6. Gai, S., Yang, G., & Zhang, S. (2013). Multiscale texture classification using reduced quaternion wavelet transform. International Journal of Electronics and Communications, 67(3), 233–241.

    Article  Google Scholar 

  7. Sedghi, T. (2013). A fast and effective model for cyclic analysis and its application in classification. Arabian Journal for Science and Engineering, 38, 927–935.

    Article  Google Scholar 

  8. Shafei, S., & Sedghi, T. (2013). Robust method for E-maximization and hierarchical clustering of image classification. Journal of Artificial Intelligence in Electrical Engineering, 6(2), 33–43.

    Google Scholar 

  9. Kalami, A., & Sedghi, T. (2013). Research article robust model of semantically rich partial understanding method for classification of face image surface information based on LDA. International Journal of Engineering & Technology, 1(3), 130–136.

    Google Scholar 

  10. Li, J., Wang, J. Z., & Wiederhold, G. G. (2000). IRM: Integrated region matching for image retrieval. In: Proceedings of the 8th ACM International Conference on Multimedia, Oct 2000, 147–156

  11. Lin, C., Chen, R., & Chan, Y. (2009). A smart content-based image retrieval system based on color and texture feature. Image and Vision Computing, 27, 658–665.

    Article  Google Scholar 

  12. Fakheri, M., Sedghi, T., Shayesteh, M. G., & Amirani, M. C. (2013). Framework for image retrieval using machine learning and statistical similarity matching techniques. IET Image Processing. https://doi.org/10.1049/iet-ipr.2012.0104

    Article  Google Scholar 

  13. Sedghi, T. (2014). High density efficient shape database classification for optimized stationary transformed features. International Journal of Engineering & Technology Sciences, 1(1), 5–11.

    Article  Google Scholar 

  14. Tannaz, S., & Sedghi, T. (2018). Image retrieval using dynamic weighting of compressed high level features framework with LER matrix. Iranian Journal of Electrical and Electronic Engineering, 14(2), 153–161.

    Google Scholar 

  15. Hor, N., & Fekri-Ershad, S. (2019). Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information. International Journal of Computer Science Engineering (IJCSE), 8(06), 246–254.

    Google Scholar 

  16. Hassan, G., Hosny, K. M., Farouk, R. M., & Alzohairy, A. M. (2020). Efficient quaternion moments for representation and retrieval of biomedical color images. Biomedical Engineering: Applications, Basis and Communications, 32(05), 2050039.

    Google Scholar 

  17. Hassan, G., Hosny, K. M., Farouk, R. M., & Alzohairy, A. M. (2020). An efficient retrieval system for biomedical images based on radial associated Laguerre moments. IEEE Access, 8, 175669–175687.

    Article  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by Tohid Sedghi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tohid Sedghi.

Ethics declarations

Conflict of interest

All of the authors do not have conflict of interest.

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

Shafei, S., Vahdati, H., Sedghi, T. et al. Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications. Wireless Pers Commun 126, 197–208 (2022). https://doi.org/10.1007/s11277-022-09740-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09740-9

Keywords

Navigation