Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Integrating machine learning algorithms for robust content-based image retrieval

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

This study introduces a robust framework for enhancing Content-Based Image Retrieval (CBIR) systems through the integration of supervised and unsupervised machine learning algorithms. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and ensemble methods like Bagging and AdaBoost, are used with unsupervised learning techniques, including K-Means and K-Medoids clustering to improve the performance of CBIR. The core of the framework leverages advanced feature extraction methods, specifically ResNet-HOG Visual Word Fusion (RVWF) and ResNet-HOG Feature Fusion (RHFF), which utilize ResNet-50 for capturing high-level semantic information and Histogram of Oriented Gradients (HOG) for detailed texture analysis. A comparison was made between the similarity-based CBIR (standalone CBIR), classification-based CBIR, and clustering-based CBIR methods. The findings reveal that classification-based CBIR methods are superior to standalone and clustering-based CBIR methods in terms of retrieval accuracy and semantic interpretation. The proposed methods outperformed the state-of-the-art methods for different databases used in this study. The proposed frameworks demonstrated superior performance across multiple databases, including VisTex, Brodatz, Corel 10K, and Corel 1K. In the VisTex database, clustering using K-Medoids-based RVWF increased performance from 98.75% to 99.52%, while classification methods like Linear Discriminant or Bagging-based RVWF achieved 100% accuracy. Similarly, in the Brodatz database, K-Medoids-based RVWF clustering improved accuracy from 97.62% to 99.62%, with classification methods such as AdaBoost or Bagging-based RVWF reaching up to 100% accuracy. For the Corel 1K and Corel 10K databases, K-Medoids-based RVWF clustering enhanced results to 95.61% and 99.20% for RVW, respectively, while classification methods further increased accuracy to 98.20% for Corel 1K and 100% for Corel 10K. These results show that combining advanced feature extraction with machine learning algorithms can improve the performance of CBIR systems. CBIR based on clustering proved to outperform standalone CBIR systems, while classification-based CBIR systems offered the best results, making them the most suitable for accurate 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

Subscribe and save

Springer+ Basic
$34.99 /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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Mentioned in the paper.

Code availability

Not applicable and could be provided.

References

  1. Kakulapati V, Pentapati V (2022) A textual framework for contour retrieval using sub-multiple contour striking spread position learning. Int J Inf Technol 14:1575–1583. https://doi.org/10.1007/s41870-020-00425-9

    Article  Google Scholar 

  2. Agarwal M (2023) Neighborhood ternary co-occurrence for natural and texture image retrieval. Int J Inf Technol 15:1999–2006. https://doi.org/10.1007/s41870-023-01238-2

    Article  Google Scholar 

  3. Tamilkodi R, Nesakumari GR (2022) Image retrieval system based on multi feature extraction and its performance assessment. Int J Inf Technol 14:1161–1173. https://doi.org/10.1007/s41870-020-00556-z

    Article  Google Scholar 

  4. Kanaparthi SK, Raju USN (2022) Content based image retrieval on big image data using local and global features. Int J Inf Technol 14:49–68. https://doi.org/10.1007/s41870-021-00806-8

    Article  Google Scholar 

  5. Ahmad K, Sahu M, Shrivastava M et al (2020) An efficient image retrieval tool: query-based image management system. Int J Inf Technol 12:103–111. https://doi.org/10.1007/s41870-018-0198-9

    Article  Google Scholar 

  6. Zhou Z, Li M, Chen H, Ma Y, Luo J (2017) A review of recent advances in content-based image retrieval. Multimed Tools Appl 76(20):21271–21311

    Google Scholar 

  7. Pang L, Du L, Han J, Ding Y (2017) A review of recent advances in content-based medical image retrieval. Healthc Technol Lett 4(2):45–52

    Google Scholar 

  8. Li X, Hu Y, Zhang L, Zhang L (2017) Deep collaborative learning for content-based image retrieval. Neurocomputing 267:630–640

    Google Scholar 

  9. Wang X, Feng Y, Wang R, Li S (2021) Unsupervised deep learning-based hashing for content-based image retrieval. J Vis Commun Image Represent 73:102934

    Google Scholar 

  10. Yousuf M, Mehmood Z, Habib HA, Mahmood T, Saba T, Rehman A, Rashid M (2018) A novel technique based on visual words fusion analysis of sparse features for effective content-based image retrieval. Math Probl Eng 2018:1–13. https://doi.org/10.1155/2018/2134395

    Article  Google Scholar 

  11. Mehmood Z, Abbas F, Mahmood T, Javid MA, Rehman A, Nawaz T (2018) Content based image retrieval based on visual words fusion versus features fusion of local and global features. Arab J Sci Eng 43(12):7265–7284. https://doi.org/10.1007/s13369-018-3062-0

    Article  Google Scholar 

  12. Ashraf R, Bashir K, Irtaza A, Mahmood M (2015) Content based image retrieval using embedded neural networks with bandletized regions. Entropy 17(6):3552–3580. https://doi.org/10.3390/e17063552

    Article  Google Scholar 

  13. Sarwar A, Mehmood Z, Saba T, Qazi KA, Adnan A, Jamal H (2019) A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine. J Inf Sci 45(1):117–135. https://doi.org/10.1177/0165551518782825

    Article  Google Scholar 

  14. Wan J (2014) Deep learning for content-based image retrieval. In: proceedings of the ACM international conference on multimedia-MM ’14, New York, USA, p 157–166

  15. Alzu’bi A, Amira A, Ramzan N (2017) Content-based image retrieval with compact deep convolutional features. Neurocomputing 249:95–105. https://doi.org/10.1016/j.neucom.2017.03.072

    Article  Google Scholar 

  16. Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neurocomputing 275:2467–2478. https://doi.org/10.1016/j.neucom.2017.11.022

    Article  Google Scholar 

  17. Zheng Q, Tian X, Yang M, Wang H (2019) Differential learning: a powerful tool for interactive content-based image retrieval. Eng Lett 27(1):202–215

    Google Scholar 

  18. Sezavar A, Farsi H, Mohamadzadeh S (2019) Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimed Tools Appl 78(15):20895–20912. https://doi.org/10.1007/s11042-019-7321-1

    Article  Google Scholar 

  19. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), LasVegas, USA, 27–30 June, p 770–778, IEEE, USA

  20. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’05), San Diego, CA, USA, 20–25 June, IEEE, USA

  21. Alrahhal M, Supreethi KP (2022) COVID-19 diagnostic system using medical image classification and retrieval: a novel method for image analysis. Comput J 65(8):2146–2163. https://doi.org/10.1093/comjnl/bxab051

    Article  Google Scholar 

  22. Alrahhal M, Supreethi K (2019) Content-based image retrieval using local patterns and supervised machine learning techniques. In: proceedings of amity international conference on artificial intelligence (AICAI), Dubai, United Arab Emirates, 4– 6 February, IEEE, Amity University, Dubai, United Arab Emirates, p 118–124

  23. Alrahhal M, Supreethi KP (2021) Full direction local neighbors pattern (FDLNP). Int J Adv Comput Sci Appl (IJACSA). https://doi.org/10.14569/IJACSA.2021.0120116

    Article  Google Scholar 

  24. Alrahhal M, Supreethi KP (2020) Multimedia image retrieval system by combining CNN with handcraft features in three different similarity measures. Int J Comput Vis Image Process 10:1–23

    Google Scholar 

  25. Corel 1000 image database. http://wang.ist.psu.edu/docs/related/

  26. Content based image retrieval/image database search engine (SIMPLIcity, WIPE, virtual microscope), Wang.ist.psu.edu, 2021. http://wang.ist.psu.edu/docs/related/

  27. Brodatz P (1996) Textures: A Photographic Album for Artist and Designers. Dover, New York

    Google Scholar 

  28. SIPI Image Database, Sipi.usc.edu, 2021. http://sipi.usc.edu/database/

  29. Index of /pub/VisTex, Vismod.media.mit.edu, 2021. https://vismod.media.mit.edu/pub/VisTex/

  30. Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: proceedings of the 18th annual ACM-SIAM symposium on discrete algorithms, 7–9 January. Society for industrial and applied mathematics, 3600 University city science center Philadelphia, PA, p 1027–1035

  31. Zhang Z (2010) A comprehensive review of the K-nearest neighbor algorithm. Advances in Artificial Intelligence. Springer, Berlin, pp 7–20

    Google Scholar 

  32. Gou J, Gao Y (2011) Improved KNN text classification algorithm based on cosine similarity. In: international conference on computer science and network technology, IEEE, p 724–727

  33. Rakotomamonjy A (2020) Support vector machines: recent trends and open problems. Neural Netw 133:97–116

    Google Scholar 

  34. Gao Q, Maji P (2021) Support vector machines: a survey. ACM Comput Surv (CSUR) 54(2):1–38

    Google Scholar 

  35. Khan F, Arif M, Saeed A (2020) Feature extraction using linear discriminant analysis and K-nearest neighbors for EEG-based emotion recognition. J Ambient Intell Humaniz Comput 11(10):4831–4843

    Google Scholar 

  36. Xu C, Yang H, Wang H (2021) A novel robust linear discriminant analysis for face recognition. Knowl Based Syst 216:106753

    Google Scholar 

  37. Chrysanthou G, Pavlou A (2020) Decision tree ensemble with bagging for detection of cybersecurity threats. Comput Secur 90:101714

    Google Scholar 

  38. Zhang X, Zhou Z, Tang Y, Li S, Wang S (2021) An improved random forest based on bagging ensemble algorithm. IEEE Access 9:20796–20811

    Google Scholar 

  39. Yang L, Jiang H, Zhao S, Zhou X (2021) AdaBoost-ResNet: a novel network traffic classification approach based on deep learning and ensemble learning. Inf Sci 559:147–166

    Google Scholar 

  40. Wang W, Sun D (2021) The improved AdaBoost algorithms for imbalanced data classification. Inf Sci 563:358–374. https://doi.org/10.1016/j.ins.2021.03.042

    Article  MathSciNet  Google Scholar 

  41. Veloso A., Reis J, Vasconcelos F, Silveira J, Abreu P, Sarmento G, Silva T, Rabêlo R (2024) Deep clustering algorithm for load profile business intelligence dashboard for consumer and utility management. Proceedings of the 20th Brazilian Symposium on Information Systems, 1–8. https://doi.org/10.1145/3658321.3658368

  42. Jha PC, Biswas R, Koley S (2022) An efficient K-means clustering algorithm for big data analytics in the internet of things. J Netw Comput Appl 195:105073

    Google Scholar 

  43. Pratap RA, Vani KS, Devi JR, Rao KN (2011) An efficient density-based improved K-Medoids clustering algorithm. Int Adv Comput Sci Appl (IJACSA) 2(6). https://doi.org/10.14569/IJACSA.2011.020607

  44. Deng J, Guo J, Wang Y, Wang Y (2019) A novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering. Knowl-Based Syst 175:12. https://doi.org/10.1016/j.knosys.2019.03.009

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. CSE Dept., JNTUH, Kukatpally, Hyderabad, Telangana, 500085, India

    Maher Alrahhal & K. P. Supreethi

Authors
  1. Maher Alrahhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. P. Supreethi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors, Maher Alrahhal and Supreethi K P, contributed equally to the work.

Corresponding author

Correspondence to Maher Alrahhal.

Ethics declarations

Conflict of interest

The authors confirm no conflict of interest.

Informed consent

Not applicable.

Research involving human and /or animals

Not applicable.

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

Alrahhal, M., Supreethi, K.P. Integrating machine learning algorithms for robust content-based image retrieval. Int. j. inf. tecnol. 16, 5005–5021 (2024). https://doi.org/10.1007/s41870-024-02169-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-024-02169-2

Keywords

Search

Navigation