Skip to main content
Log in

Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript


Content-based image retrieval (CBIR) has made significant advancements and continues to evolve with the rapid development of computer vision and artificial intelligence technologies. Feature plays vital role in CBIR systems since its main components are color, texture, and shape. Generation of the feature descriptor is a crucial part in CBIR due to its efficient exploration of image characteristics. Feature descriptors have the ability to reduce the feature dimension and enhance the image retrieval rate in CBIR. Hence, in this paper, a novel CBIR system is proposed named ‘Novel CBIR method based on features of correlated Visual Textons and MQLPP Descriptor (CBIR_VTMD)’, which aims high retrieval performance via finer feature extraction. The main contribution in the proposed method is a new texture descriptor namely ‘Multi-channel Quantized Local Penta Pattern based image descriptor (MQLPP)’ which expresses a new perspective to the enhancement of foreground object feature in an image. One of the significant traits of the MQLPP descriptor is the ‘utilization of innovative penta pattern using multi channels’. Proposed CBIR_VTMD method is experimented using well-known databases such as COREL-10K, CTMOS, ESAT, VISTEX, INDOOR, and DERMO. The experimental results reveal that the proposed CBIR_VTMD method enhances up to 10.7497% retrieval accuracy when compared to the state-of-the-arts method. The proposed method acts as the generic CBIR which efficaciously delivers retrieval results for the domains that uses natural, medical, remote sensing, and texture images. Besides, the proposed CBIR_VTMD framework works better in both online and offline real-time applications.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data Availability

The data and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request. The public databases that support the findings of this study especially for testing and training are openly available as follows: Corel-10 k at, [32]. Indoor Scene Recognition at, [33]. CT volumes with multiple organ segmentations (CT-ORG) at, [34]. PH2 at, [35]. Euro Sat at, [36]. Vision Texture at, [37].


  1. Brogan J, Bharati A, Moreira D, Rocha A, Bowyer KW, Flynn PJ, et al. Fast local spatial verification for feature-agnostic large-scale image retrieval. IEEE Trans Image Process. 2021;30:6892–905.

    Article  Google Scholar 

  2. Xie G, Guo B, Huang Z, Zheng Y, Yan Y. Combination of dominant color descriptor and Hu moments in consistent zone for content based image retrieval. IEEE Access. 2020;8:146284–99.

    Article  Google Scholar 

  3. Ahmed A, Malebary SJ. Query expansion based on top-ranked images for content-based medical image retrieval. IEEE Access. 2020;8:194541–50.

    Article  Google Scholar 

  4. Staszewski P, Jaworski M, Cao J, Rutkowski L. A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers. IEEE Trans Neural Netw Learn Syst. 2022;33(12):7913–20.

    Article  MathSciNet  Google Scholar 

  5. Zhang J, Ye L. Content based image retrieval using unclean positive examples. IEEE Trans Image Process. 2009;18(10):2370–5.

    Article  MathSciNet  Google Scholar 

  6. Li JS, Liu IH, Tsai CJ, Su ZY, Li CF, Liu CG. Secure content-based image retrieval in the cloud with key confidentiality. IEEE Access. 2020;8:114940–52.

    Article  Google Scholar 

  7. Sumbul G, Ravanbaksh M, Demir B. Informative and representative triplet selection for multilabel remote sensing image retrieval. IEEE Trans Geosci Remote Sens. 2021;60:5405811.

    Article  Google Scholar 

  8. Sikha OK, Soman KP. Dynamic Mode Decomposition based salient edge/region features for content based image retrieval. Multimed Tools Appl. 2021;80:15937–58.

    Article  Google Scholar 

  9. Rashad M, Afifi I, Abdelfatah M. RbQE: an efficient method for content-based medical image retrieval based on query expansion. J Digit Imaging. 2023;36:1248–61.

    Article  Google Scholar 

  10. Liu Y, Dhakal S, Hao B. Multimedia image and video retrieval based on an improved HMM. Multimed Syst. 2022;28:2093–103.

    Article  Google Scholar 

  11. Rajasenbagam T, Jeyanthi S, Pandian JA. Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques. J Ambient Intell Human Comput. 2021.

    Article  Google Scholar 

  12. Kumar S, Pradhan J, Pal AK. Adaptive tetrolet based color, texture and shape feature extraction for content based image retrieval application. Multimed Tools Appl. 2021;80(19):29017–49.

    Article  Google Scholar 

  13. Garg M, Dhiman G. A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Comput Appl. 2021;33:1311–28.

    Article  Google Scholar 

  14. Desai P, Pujari J, Akhila, Sujatha C. Impact of multi-feature extraction on image retrieval and classification using machine learning technique. SN Comput. Sci. 2021;2(3):153.

  15. Ghodratnama S, Moghaddam HA. Content-based image retrieval using feature weighting and C-means clustering in a multi-label classification framework. Pattern Anal Applic. 2021;24:1–10.

    Article  Google Scholar 

  16. Majhi M, Pal AK. An image retrieval scheme based on block level hybrid DCT-SVD fused features. Multimed Tools Appl. 2021;80:7271–312.

    Article  Google Scholar 

  17. Dubey SR, Singh SK, Singh RK. Multichannel decoded local binary patterns for content based image retrieval. IEEE Trans Image Process. 2016;25(9):4018–32.

    Article  MathSciNet  Google Scholar 

  18. Raza A, Dawood H, Dawood H, Shabbir S, Mehboob R, Banjar A. Correlated primary visual texton histogram features for content base image retrieval. IEEE Access. 2018;6:46595–616.

    Article  Google Scholar 

  19. Wei W, Wang Y. Color image retrieval based on quaternion and deep features. IEEE Access. 2019;7:126430–8.

    Article  Google Scholar 

  20. Dawood H, Alkinani MH, Raza A, Dawood H, Mehboob R, Shabbir S. Correlated microstructure descriptor for image retrieval. IEEE Access. 2019;7:55206–28.

    Article  Google Scholar 

  21. Asif MDA, Wang J, Gao Y, et al. Composite description based on color vector quantization and visual primary features for CBIR tasks. Multimed Tools Appl. 2021;80:33409–27.

    Article  Google Scholar 

  22. Kabir MM, Ishraq A, Nur K, Mridha MF. Content-based image retrieval using AutoEmbedder. J Adv Inf Technol. 2022;13(3):240–8.

    Article  Google Scholar 

  23. Raibagkar RL, Shaheen F. Efficient content-based image retrieval system with two-tier hybrid frameworks. Appl Comput Syst. 2023;27(2):166–82.

    Article  Google Scholar 

  24. Bu HH, Kim NC, Kim SH. Content-based image retrieval using a fusion of global and local features. ETRI J. 2023;45:505–18.

    Article  Google Scholar 

  25. Patel B, Yadav K, Ghosh D. State-of-art: similarity assessment for content based image retrieval system. In: 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). 2020.

  26. Patil S, Talbar S. Content based image retrieval using various distance metrics. In: Data engineering and management. 2012;154–161.

  27. Missaoui R, Sarifuddin M, Vaillancourt J. Similarity measures for efficient content-based image retrieval. IEE Proc Vis Image Signal Process. 2005;152(6):875–87.

    Article  Google Scholar 

  28. Alsmadi MK. An efficient similarity measure for content based image retrieval using memetic algorithm. Egypt J Basic Appli Sci. 2017;4(2):112–22.

    Article  Google Scholar 

  29. Alsmadi MK. Query-sensitive similarity measure for content-based image retrieval using meta-heuristic algorithm. J King Saud Univ Comput Inf Sci. 2018;30(3):373–81.

    Article  Google Scholar 

  30. Blanco G, Bedo MVN, Cazzolato MT, Santos LFD, Jorge AES, Traina C, et al. A label-scaled similarity measure for content-based image retrieval. 2016 IEEE International Symposium on Multimedia (ISM). 2016.

  31. Euclidean distance., 2023. [Online; accessed 21-August-2023].

  32. Corel-10k., 2023. [Online; accessed 4-August-2023].

  33. Indoor Scene Recognition., 2009.

  34. CT-ORG., 2023. [Online; accessed 4-August-2023].

  35. PH2., 2013. [Online; accessed 4-August-2023].

  36. Euro Sat., 2021. [Online; accessed 4-August-2023].

  37. Vision Texture., 2002. [Online; accessed 4-August-2023].

Download references


No funds, grants, or other support was received.

Author information

Authors and Affiliations



All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by J. Anto Germin Sweeta and Dr. B. Sivagami. The first draft of the manuscript was written by J. Anto Germin Sweeta and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to J. Anto Germin Sweeta.

Ethics declarations

Conflict of interest

The authors declare that this article has no conflict of interest.

Ethics Approval

This study does not contain any studies with human or animal subjects performed by any of the authors.

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

Sweeta, J.A.G., Sivagami, B. Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor. SN COMPUT. SCI. 5, 666 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: