Skip to main content
SpringerLink
Log in

Semantic-Based Image Retrieval Using \(R^S\)-Tree and Neighbor Graph

  • Conference paper
  • First Online:
Information Systems and Technologies (WorldCIST 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 469))

Included in the following conference series:

Abstract

In this paper, we present a semantic-based image retrieval method using \(R^S\)-Tree structure and neighbor graph. The main ideas of the paper include (1) proposing a structure by combining of \(R^S\)-Tree and neighbor clustering graph, named NBGraphRST; (2) enriching an ontology framework to describe the semantic feature of images. Firstly, a query image is extracted as a low-level visual feature vector and retrieved on NBGraphRST to get a set of content-based similar images. Secondly, the input images are classified by the k-nearest neighbor (k-NN) method to create a set of visual vocabularies. Finally, the SPARQL query is generated to retrieve the semantics of similar images based on ontology. On the basis of the proposed method, the experiment is performed on data-sets COREL, Wang, Oxford Flowers-17.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Kaur, H., Jyoti, K.: Survey of techniques of high level semantic based image retrieval. Pattern Recogn. 45(1), 346–362 (2012)

    Article  Google Scholar 

  2. Simou, N., Athanasiadis, T., Stoilos, G., Kollias, S.: Image indexing and retrieval using expressive fuzzy description logics. Sig. Image Video Process 2(4), 321–335 (2008)

    Article  Google Scholar 

  3. Shaik, A.N.B., Supreethi, K.P.: A survey on spatial indexing. J. Web Dev. Web Des. 3(1), 1–25 (2018)

    Google Scholar 

  4. Manolopoulos, Y., Papadopoulos, A.N., Papadopoulos, A.N., Theodoridis, Y.: Introduction. In: Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A.N., Theodoridis, Y. (eds.) R-Trees: Theory and Applications, pp. 117–125. Springer, London (2010)

    Google Scholar 

  5. Yan, S., Zhang, M., Lai, S., Liu, Y., Peng, Y.: Image retrieval for structure-from-motion via graph convolutional network. Inf. Sci. 573, 20–36 (2021)

    Article  MathSciNet  Google Scholar 

  6. Pedronette, D.C.G., Weng, Y., Baldassin, A., Hou, C.: Semi-supervised and active learning through manifold reciprocal kNN graph for image retrieval. Neurocomputing 340, 19–31 (2019)

    Article  Google Scholar 

  7. Li, W., Duan, L., Xu, D., Tsang, I.W.H.: Text-based image retrieval using progressive multi-instance learning. In: International Conference on Computer Vision, pp. 2049–2055. IEEE (2011)

    Google Scholar 

  8. Douik, A., Abdellaoui, M., Kabbai, L.: Content based image retrieval using local and global features descriptor. In: 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 151–154. IEEE (2016)

    Google Scholar 

  9. Park, K.W., Jeong, J.W., Lee, D.H.: OLYBIA: ontology-based automatic image annotation system using semantic inference rules. In: International Conference on Database Systems for Advanced Applications (2007)

    Google Scholar 

  10. Filali, J., Zghal, H., Martinet, J.: Towards visual vocabulary and ontology-based image retrieval system. In: International Conference on Agents and Artificial Intelligence, vol. 2, pp. 560–565 (2016)

    Google Scholar 

  11. Vanitha, J., SenthilMurugan, M.: An efficient content based image retrieval using block color histogram and color co-occurrence matrix. Int. J. Appl. Eng. Res. 15966–15971 (2017)

    Google Scholar 

  12. Gonçalves, F.M.: Semantic guided interactive image retrieval for plant identification. Expert Syst. Appl. 91, 12–26 (2018)

    Article  Google Scholar 

  13. Asim, M.N., Wasim, M., Khan, M.U.G., Mahmood, N., Mahmood, W.: The use of ontology in retrieval: a study on textual, multilingual, and multimedia retrieval. IEEE Access 7, 21662–21686 (2019)

    Article  Google Scholar 

  14. Al-Mohamade, A., Bchir, O., Ben Ismail, M.M.: Multiple query content-based image retrieval using relevance feature weight learning. J. Imaging 6(1), 2 (2020)

    Article  Google Scholar 

  15. Huneiti, A., Daoud, M.: Content-based image retrieval using SOM and DWT. J. Softw. Eng. Appl. 8(02), 51 (2015)

    Article  Google Scholar 

  16. Nhi, N.T.U., Thanh, T.V., Le, T.M.: Semantic-based image retrieval using balanced clustering tree. In: WorldCIST, vol. 2, pp. 416–427 (2021)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank the Faculty of Information Technology, Universi-ty of Sciences - Hue University for their professional advice for this study. We would also like to thank HCMC University of Food Industry, Ba Ria - Vung Tau University, University of Education HCMC, and research group SBIR HCM, which are sponsors of this research. We also would like to express our sincere thanks to reviewers for their helpful comments on this article.

Author information

Authors and Affiliations

  1. University of Sciences - Hue University, Hue, Vietnam

    Le Thi Vinh Thanh & Le Manh Thanh

  2. HCMC University of Education, Ho Chi Minh City, Vietnam

    Van The Thanh

  3. Ba Bia - Vung Tau University, Ba Ria - Vung Tau City, Vietnam

    Le Thi Vinh Thanh

Authors
  1. Le Thi Vinh Thanh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Le Manh Thanh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Van The Thanh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Van The Thanh .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisboa, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Te, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, PORTO, Portugal

    Fernando Moreira

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thanh, L.T.V., Thanh, L.M., Thanh, V.T. (2022). Semantic-Based Image Retrieval Using \(R^S\)-Tree and Neighbor Graph. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-04819-7_18

Download citation

Publish with us

Policies and ethics

Search

Navigation