Skip to main content
Log in

Comparative analysis of color histogram and LBP in CBIR systems

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In the current trend, the image retrieval (IR) system has also been shifted from traditional text-based to content-based with the advancement in technology. Many issues have been resolved by content-based IR. In a particular case of information retrieval/image retrieval (IR) systems, the number of images on the web and the usage of images are growing exponentially. Therefore, IR system needs to be scalable, flexible, modularizable and promotes reusability so that it becomes easy to deploy, develop and maintain the system. In this paper, we have presented a CBIR model using Color Histogram and Local Binary Pattern (LBP) where both are built with Microservices architecture using docker platform. The framework used in this model is logic independent therefore any CBIR system can be run using this framework. The CBIR using Color Histogram uses chi-squared distance as a similarity measure while CBIR model using LBP is implemented using Linear Support Vector Machines for image classification. In our experiments, we have achieved the average recall, precision, and F-measure using Color Histogram 22.25%, 63.12%, and 32.67%, respectively. Though, we have achieved the average recall, precision, and F-measure using LBP 77.15%, 79.90%, and 76.17%, respectively. It has been observed that LBP model is more accurate than Color Histogram for detecting different weather conditions. It has also been found that the use of Microservices architecture leads to improve the non-functional qualities of a CBIR system as compared to traditional architecture styles by a great margin.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

We have used the publically available dataset. The following weblink can be used to access the Corel dataset:

1. https://www.kaggle.com/datasets/elkamel/corel-images.

References

  1. Aderaldo CM, Mendonça NC, Pahl C, Jamshidi P (2017) Benchmark requirements for microservices architecture research. In: 2017 IEEE/ACM 1st International Workshop on Establishing the Community-Wide Infrastructure for Architecture-Based Software Engineering (ECASE). pp 8–13

  2. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  3. Ali WK, Mohammed AA, Fuad N (2020) Color based image retrieval system using histogram. J Crit Rev 7(14):4207–4216

    Google Scholar 

  4. Bhunia AK, Bhattacharyya A, Banerjee P, Roy PP, Murala S (2020) A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Anal Appl 23(2):703–723

    Article  Google Scholar 

  5. Boudra S, Yahiaoui I, Behloul A (2021) A set of statistical radial binary patterns for tree species identification based on bark images. Multimed Tools Appl 80(15):22373–22404

    Article  Google Scholar 

  6. Degaonkar VN, Gadakh P, Saha P, Kulkarni AV (2020) Retrieve content images using color histogram, LBP and HOG. In: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA). pp 896–899

  7. Ghahremani M, Ghadiri H, Hamghalam M (2021) Local features integration for content-based image retrieval based on color, texture, and shape. Multimed Tools Appl 80(18):28245–28263

    Article  Google Scholar 

  8. Grycuk R, Najgebauer P, Nowicki R, Scherer R (2019) Multilayer architecture for content-based image retrieval systems. In: 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA). IEEE, pp 119–126

  9. Jaramillo D, Nguyen DV, Smart R (2016) Leveraging microservices architecture by using Docker Technology. SoutheastCon 2016:1–5

    Google Scholar 

  10. Kapoor R, Sharma D, Gulati T (2021) State of the art content based image retrieval techniques using deep learning: a survey. Multimed Tools Appl 80(19):29561–29583

    Article  Google Scholar 

  11. Kashyap N, Singh DK (2017) Color histogram based image retrieval technique for diabetic retinopathy detection. In: 2017 2nd International Conference for Convergence in Technology (I2CT). IEEE, pp 799–802

  12. Kayhan N, Fekri-Ershad S (2021) Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimed Tools Appl 80(21):32763–32790

    Article  Google Scholar 

  13. Khan A, Javed A, Mahmood MT, Khan MHA, Lee IH (2021) Directional magnitude local hexadecimal patterns: a novel texture feature descriptor for content-based image retrieval. IEEE Access 9:135608–135629

    Article  Google Scholar 

  14. Komazec T, Gavrovska A (2020) Photo matching using skin color histogram without and with instagram-like modifications. In: 2020 28th Telecommunications Forum (TELFOR). IEEE, pp 1–4

  15. Kumar AR, Saravanan D (2013) Content based image retrieval using color histogram. Int J Comput Sci Inf Technol 4(2):242–245

    Google Scholar 

  16. Lehmann TM, Güld MO, Thies C, Fischer B, Spitzer K, Keysers D, Ney H, Kohnen M, Schubert H, Wein BB (2004) Content-based image retrieval in medical applications. Methods Inf Med 43(04):354–361

    Article  Google Scholar 

  17. Marée R, Denis P, Wehenkel L, Geurts P (2010) Incremental indexing and distributed image search using shared randomized vocabularies. In: Proceedings of the International Conference on Multimedia Information Retrieval. pp 91–100

  18. Meena M, Singh AR, Bharadi VA (2016) Architecture for software as a service (SAAS) model of CBIR on hybrid cloud of Microsoft azure. Procedia Comput Sci 79:569–578

    Article  Google Scholar 

  19. Moustakas J, Marias K, Dimitriadis S, Orphanoudakis SC (2005) A two-level CBIR platform with application to brain MRI retrieval. In: 2005 IEEE International Conference on Multimedia and Expo. IEEE, pp 1278–1281

  20. Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886

    Article  MathSciNet  Google Scholar 

  21. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  22. Rosebrock A (2014) The complete guide to building an image search engine with Python and opencv. https://www.pyimagesearch.com/2014/12/01/complete-guide-building-image-search-engine-python-opencv/

  23. Shi D, Tang H (2021) Face recognition algorithm based on self-adaptive blocking local binary pattern. Multimed Tools Appl 80(16):23899–23921

    Article  Google Scholar 

  24. Singha M, Hemachandran K (2012) Content based image retrieval using color and texture. Sig Img Proc 3(1):39

    Google Scholar 

  25. Trojacanec K, Dimitrovski I, Loskovska S (2009) Content based image retrieval in medical applications: an improvement of the two-level architecture. In: IEEE EUROCON 2009. IEEE, pp 118–121

  26. Yuan Y, Zhang W, Yu H, Zhu Z (2021) Superpixels with content-adaptive criteria. IEEE Trans Image Process 30:7702–7716

    Article  Google Scholar 

  27. Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjeev Patel.

Ethics declarations

Competing interest

We declare that we do not have any competing financial interests and also do not have any personal relationships that could have influenced this work.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dowerah, R., Patel, S. Comparative analysis of color histogram and LBP in CBIR systems. Multimed Tools Appl 83, 12467–12486 (2024). https://doi.org/10.1007/s11042-023-15955-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15955-0

Keywords

Navigation