Skip to main content
SpringerLink
Log in

Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retrieved, its retrieval efficiency still cannot meet people’s needs. In this paper, we propose an image retrieval algorithm based on the combination of color and shape features. The cumulative histogram method is used to calculate the color features of the image, and 7 Hu invariant moments are calculated as shape features. The color and shape features are combined with certain weights, and the Euclidean distance is used as the similarity measure. Finally, the image is retrieved, and the related experiments are passed. By comparing with related experiments, the algorithm effectively improves the accuracy of image retrieval.

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
Fig. 4

Similar content being viewed by others

References

  1. Qiu, H., Noura, H., Qiu, M., Ming, Z., & Memmi, G. (2019). A user-centric data protection method for cloud storage based on in vertible DWT[J]. IEEE Transactions on Cloud Computing, early access 1–1.

  2. Qiu, H., Memmi, G., Chen, X., & Xiong, J. (2019). DC coefficient recovery for JPEG images in ubiquitous communication systems[J]. Future Generation Computer Systems, 96, 23–31.

    Article  Google Scholar 

  3. Qiu, H., Kapusta, K., Lu, Z., Qiu, M., & Memmi, G. (2019). All-or-nothing data protection for ubiquitous communication: Challenges and perspectives[J]. Information Sciences, 502, 434.

    Article  MathSciNet  Google Scholar 

  4. Gandhani, S., & Singhal, N. (2015). Content based image retrieval: Survey and comparison of CBIR system based on combined features[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(10), 155–162.

    Article  Google Scholar 

  5. Qiu, M., Sha, E. H. M., Liu, M., Lin, M., Hua, S., & Yang, L. T. (2008). Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP[J]. Journal of Parallel and Distributed Computing, 68(4), 443–455.

    Article  Google Scholar 

  6. Shao, Z., Wang, M., Chen, Y., Xue, C., Qiu, M., Yang, L., & Sha, E. (2007). Real-time dynamic voltage loop scheduling for multi-core embedded systems[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 54(5), 445–449.

    Article  Google Scholar 

  7. Wang, J., Qiu, M., & Guo, B. (2017). Enabling real-time information service on telehealth system over cloud-based big data platform[J]. Journal of Systems Architecture, 72, 69–79.

    Article  Google Scholar 

  8. Li, J., Qiu, M., Niu, J., Gao, W., Zong, Z., & Qin, X. (2010). Feedback dynamic algorithms for preemptable job scheduling in cloud systems [C]. In 2010 IEEE/WIC/ACM international conference on web intelligence (pp. 561–564). IEEE Computer Society.

  9. Qiu, M., Jia, Z., Xue, C., Shao, Z., & Sha, E. H. M. (2007). Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP[J]. The Journal of VLSI Signal Processing Systems, 46(1), 55–73.

    Article  Google Scholar 

  10. Swain, M. J., & Ballard, D. H. (1991). Color indexing[J]. International Journal of Computer Vision, 7(1), 11–32.

    Article  Google Scholar 

  11. Pass, G., Zabih, R., & Miller, J. (1996). Comparing images using color coherence vectors[C] (Vol. 96, pp. 65–73). ACM multimedia.

  12. Stricker, M. A., & Orengo, M. (1995). Similarity of color images[C]. Storage and retrieval for image and video databases III (Vol. 2420, pp. 381–392). International Society for Optics and Photonics.

  13. Heng, H. E., & Lin, Y. Y. (2001). Image retrieval using combined fuzzy histogram[J]. Journal of Image and Graphics, 7(106), 84–88.

  14. Talib, A., Mahmuddin, M., Husni, H., & George, L. E. (2013). A weighted dominant color descriptor for content-based image retrieval[J]. Journal of Visual Communication and Image Representation, 24(3), 345–360.

    Article  Google Scholar 

  15. Walia, E., & Pal, A. (2014). Fusion framework for effective color image retrieval[J]. Journal of Visual Communication and Image Representation, 25(6), 1335–1348.

    Article  Google Scholar 

  16. Zeng, S., Huang, R., Wang, H., & Kang, Z. (2016). Image retrieval using spatiograms of colors quantized by Gaussian mixture models[J]. Neurocomputing, 171, 673–684.

    Article  Google Scholar 

  17. Gai, K., Qiu, M., Xiong, Z., & Liu, M. (2018). Privacy-preserving multi-channel communication in edge-of-things[J]. Future Generation Computer Systems, 85, 190–200.

    Article  Google Scholar 

  18. Li, J., Qiu, M., Niu, J., Gao, W., Zong, Z., & Qin, X. (2010). Feedback dynamic algorithms for preemptable job scheduling in cloud systems[C]. In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 1, pp. 561–564). IEEE.

  19. Sundara Vadivel, P., Yuvaraj, D., Navaneetha Krishnan, S., & Mathusudhanan, S. R. (2019). An efficient CBIR system based on color histogram, edge, and texture features[J]. Concurrency and Computation: Practice and Experience, 31(12), e4994.

    Article  Google Scholar 

  20. Dhou, K. (2019). An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach[J]. Applied Soft Computing, 79, 94–110.

    Article  Google Scholar 

  21. Lisheng, R., & Lizhong, W. (2016). Analysis of image matching algorithm for corner detection based on curvature scale space[J]. Electronic Technology Application, 42(12), 112–114.

    Google Scholar 

  22. XU Qiang, & MA Dengwu. (2014). Classification tree of contours based on Fourier descriptor’s peincipal coefficients[J]. Journal of Computer Applications, 34(A01), 124–126.

    Google Scholar 

  23. Yongku, Z., Yunfeng, L. I., & Jingguang, S. (2014). Image retrieval 473based on clustering according to color and shape features[J]. Journal of Computer Applications, 34(12), 3549–3553..

  24. Lan, R., Guo, S., & Jia, S. (2018). Criminal investigation image retrieval algorithm based on texture and shape feature fusion[J]. Computer Engineering and Design, 39(4), 1106–1110.

    Google Scholar 

  25. Imran, M., Hashim, R., & Khalid, N. E. A. (2014). Segmentation-based fractal texture analysis and color layout descriptor for content based image retrieval[C]. In 2014 14th international conference on intelligent systems design and applications (pp. 30–33). IEEE.

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61972136, No. 61471161, No. 61971339), Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (T201410), Hubei Province Higher Education Teaching Research Project(No.2018432).

Author information

Authors and Affiliations

  1. School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, Hubei, China

    Xiong Zenggang, Tang Zhiwen, Chen Xiaowen, Zhang Xue-min & Ye Conghuan

  2. School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, Hubei, China

    Xiong Zenggang & Tang Zhiwen

  3. School of Electronics and Information, Xian Polytechnic University, Xian, 710048, Shanxi, China

    Zhang Kaibin

Authors
  1. Xiong Zenggang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tang Zhiwen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Xiaowen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhang Xue-min
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhang Kaibin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ye Conghuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Xiaowen.

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

Zenggang, X., Zhiwen, T., Xiaowen, C. et al. Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features. J Sign Process Syst 93, 139–146 (2021). https://doi.org/10.1007/s11265-019-01508-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-019-01508-y

Keywords

Search

Navigation