Skip to main content
SpringerLink
Log in

Content-Based Image Retrieval (CBIR): A Review

  • Conference paper
  • First Online:
Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 855))

Abstract

This paper gives an overview of various CBIR (Content-Based Image Retrieval) Techniques. CBIR is a system that utilizes different image features, like texture, color, and shape information, to fetch different images from a huge database. CBIR systems are mostly used in the medical field as they collect images from a huge database depending on the similarities. This aids in diagnosing patients. The paper describes CBIR as the first step followed by addressing the characteristics of an image (i.e., image features). Further, the techniques and applications of CBIR are discussed. The recent papers described in literature review are used to enhance the study of CBIR-based applications. This paper is useful for the researchers willing to pursue their research in content-based image retrieval.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. P.A. Dahake, S.S. Thakare, Content based image retrieval: a review. Int. Res. J. Eng. Technol. (IRJET) 05(01) (2018)

    Google Scholar 

  2. S. Rubini, R. Divya, G. Divyalakshmi, T.M.S. Ganesan, Content based image retrieval (CBIR). Int. Res. J. Eng. Technol. (IRJET) 05(03) (2018)

    Google Scholar 

  3. P. Kaur, R.K. Singh, Content based image retrieval using machine learning and soft computing techniques. Int. J. Sci. Technol. Res. 9(1) (2020)

    Google Scholar 

  4. R.U. Rani, Image qualification learning technique through content based image retrieval. Int. J. Future Revolut. Comput. Sci. Commun. Eng. 4(1) (2020)

    Google Scholar 

  5. V. Ramya, Content based image retrieval system using clustering with combined patterns. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 3(1), 1060–1063 (2018)

    Google Scholar 

  6. S.S. Tadasare, S.S. Pawar, Content based retinal image retrieval using lifting wavelet transform for classification of retinal fundus images. Int. J. Elect. Electron. Comput. Sci. Eng. 5(1), 169–176 (2018)

    Google Scholar 

  7. D. Sharma, T. Gulati, R. Sharma, Rotation invariant content based image retrieval system for medical images. Int. J. Future Revolut. Comput. Sci. Commun. Eng. 4(3), 171–174 (2018)

    Google Scholar 

  8. X.Y. Wang, H.Y. Yang, D.M. Li, A new content based image retrieval technique using color and texture information. Comput. Electr. Eng. 39(3), 746–761 (2013)

    Google Scholar 

  9. V. Yadaiah, R. Vivekanandam, R. Jatothu, A Fuzzy logic based soft computing approach in CBIR system using incremental filtering feature selection to identify patterns. Int. J. Appl. Eng. Res. 13, 2432–2442 (2018)

    Google Scholar 

  10. A. Rao Nagar, N.S. Sushmitha, M.K. Nalini, Content based image retrieval for medical domain. Int. J. Eng. Res. Technol. 8(06), 1345–1351 (2019)

    Google Scholar 

  11. R. Rajkumar, M.V. Sudhamani, Content based image retrieval system using combination of color and shape features, and siamese neural network. Int. J. Innovat. Technol. Explor. Eng. 9, 71–77 (2019)

    Google Scholar 

  12. P. Kaur, D. Kumar Singh, Content based image retrieval using machine learning and soft computing techniques. Int. J. Sci. Technol. Res. 9(1), 1327–1332 (2020)

    Google Scholar 

  13. H. Jun, B. Ko, Y. Kim, L. Kim, J. Kim, Combination of multiple global descriptors for image retrieval (2020)

    Google Scholar 

  14. K. Chu, G. Hai Liu, Image retrieval based on a multi- integration features model (2020)

    Google Scholar 

  15. K.S. Aiswarya, N. Santhi, K. Ramar, Content based image retrieval for mobile devices using multi- stage autoencoders. J. Crit. Rev. (2020)

    Google Scholar 

  16. R. Brunelli, O. Mich, Histogram analysis for image retrieval. Pattern Recogn. 34(8), 1625–1637 (2001)

    Article  Google Scholar 

  17. M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies, Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205–221 (1992)

    Article  Google Scholar 

  18. C.C. Gotlieb, H.E. Kreyszig, Texture descriptors based on co-occurrence matrices. Comput. Vision Gr. Image Process. 51(1), 70–86 (1990)

    Google Scholar 

  19. R.N. Bracewell, The fourier transform. Sci. Am. Div. Nat. Am. Inc. 260(6), 86–95 (1989)

    Google Scholar 

  20. B. M. Mehtre, M.S. Kankanhalli, W. Foon Lee, Shape measures for content based image retrieval: a comparison. Inf. Process. Manag. 33(3), 319–337 (1997)

    Google Scholar 

  21. S. Han, S. Yang, S, An invariant feature representation for shape retrieval. in Proceedings of Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (2005)

    Google Scholar 

  22. K. Orth, Estimation methods for models of spatial interaction. J. Am. Stat. Assoc 70(349), 120–126 (1975)

    Google Scholar 

  23. A. Witkin, Scale-space filtering: a new approach to multi-scale description. in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 9 (1984). pp. 19–21

    Google Scholar 

  24. M.R. Teague, Image analysis via the general theory of moments. Opt. Soc. Am. J. 70, 920–930 (1980)

    Google Scholar 

  25. W.Y. Kim, Y.S Kim, A region based shape descriptor using Zernike moments. Signal Process. Image Commun. 16(1), 95–102 (2000)

    Google Scholar 

  26. E. Candes, L. Demanet, D. Donoho, L. ying, Fast discrete curvelet transforms. Soc. Indust. Appl. Math. 5(3), 861–899 (2006)

    Google Scholar 

  27. J. Almeida, R.S. Torres, S. Goldenstein, SIFT applied to CBIR. Revista de Sistemas de Informacao da FSMA n. 4, 41–48 (2009)

    Google Scholar 

  28. M. Kamath, D. Punjabi, T. Sabnis, D. Upadhyay, S. Shrawne, Improving content based image retrieval using scale invariant feature transform. Int. J. Eng. Adv. Technol. (IJEAT) 1(5), 2249–8958 (2012)

    Google Scholar 

  29. Z. Wang, K. Jia, P. Liu, An effective web content-based image retrieval algorithm by using SIFT feature. In: World Congress on Software Engineering, IEEE Computer Society (2009).

    Google Scholar 

  30. R.A. Konstantinos, K.S. Ntalianis, D.D. Sourlas, S.D. Kollias, Mining user queries with Markov Chains: application to online image retrieval. IEEE Trans. Knowl. Data Eng. 25(2) (2013)

    Google Scholar 

  31. S. Kulkarni, P. Kulkarni, Color image annotation using hybrid intelligent techniques for image retrieval. IEEE Int. Conf. (2012). 978-1-4673-5116-4/12

    Google Scholar 

  32. F. Debole, C. Gennaro, P. Savino, Enriching image feature description supporting effective content based retrieval and annotation. IEEE Int. Conf. (2014)

    Google Scholar 

  33. M. Eitz, M. Hildebrand, T. Boubekeur, M. Alexa, Sketch based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans. Visualization Comput. Gr. 17(11) (2011)

    Google Scholar 

  34. Y. He, L. Yang, Y. Zhang, X. Wu, Y. Chang, The binary image retrieval based on the improved shape context. in 7th International Conference on Image and Signal Processing (2014)

    Google Scholar 

  35. E.R. Vinima, K.J. Poulas, Image retrieval using color and texture features of regions of interest. in IEEE Conference (2012)

    Google Scholar 

  36. H.R. Tizhoosh, Barcode annotations for medical image retrieval: a preliminary investigation. in IEEE International Conference. 978–1–4799-8339–1/15 (2015)

    Google Scholar 

  37. G. Schroth, S. Hilsenbeck, R. Huitl, F. Schweiger, E. Steinbach, Exploiting text-related features for content based image retrieval. in IEEE International Symposium on Multimedia (2011)

    Google Scholar 

  38. K.J. Hsiao, J. Calder, A.O. Hero, Pareto-depth for multiple query image retrieval. IEEE Trans. Image Process. 24(2) (2015)

    Google Scholar 

  39. Z. Su, H. Zhang, S. Li, S. MA, Relevance feedback in content based image retrieval: bayesian framework, feature subspaces, and progressive learning. IEEE Trans. Image Process. 12(8) (2003)

    Google Scholar 

  40. A. Khokher, R. Talwar, Content-based image retrieval: feature extraction techniques and applications. in International Conference on Recent Advances and Future Trends in Information Technology (2012) pp. 9–14

    Google Scholar 

  41. R.R Saritha, V. Paul, P.G. Kumar, Content based image retrieval using deep learning process. Clust. Comput. 1–14 (2018)

    Google Scholar 

  42. R. Gross, J. Shi, J. Cohn, Quo Vadis Face Recognition: Third Workshop on Empirical Evaluation methods in Computer Vision, Pittsburg (Carnegie Mellon University, USA, 2001)

    Google Scholar 

  43. D. Zhang, M.M. Islam, G. Lu, A review on automatic image annotation techniques. Pattern Recognit. 45(1), 346–362 (2012)

    Google Scholar 

  44. L. Wang, Y. Zhang, J. Feng, On the Euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334–1339

    Google Scholar 

  45. G. Jurman, S. Riccadonna, R. Visintainer, C. Furlanello, Canberra distance on ranked lists. in Proceedings of Advances in Ranking NIPS 09 Workshop (2009), pp. 22–27

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, UP, India

    Deepti Agrawal, Apurva Agarwal & Dilip Kumar Sharma

Authors
  1. Deepti Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Apurva Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dilip Kumar Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Department of CSE, Central University of Jammu, Jammu and Kashmir, India

    Yashwant Singh

  3. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Jitender Kumar Chhabra

  4. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Zoltán Illés

  5. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Chaman Verma

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agrawal, D., Agarwal, A., Sharma, D.K. (2022). Content-Based Image Retrieval (CBIR): A Review. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_33

Download citation

Publish with us

Policies and ethics

Search

Navigation