Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Distributed Computing and Artificial Intelligence

DCAI 2018: Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference pp 87–95Cite as

World Wide Web CBIR Searching Using Query by Approximate Shapes

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 801))

Abstract

Nowadays more and more images are stored in the World Wide Web. There are a lot of photo galleries, media portals and social media portals where users add their own content, but also they would like to find the proper ones. The problem of searching for an image is not trivial. Objects present on images may have e.g. different colors, backgrounds or orientations. Moreover, the image may contain many other details which may be hard to be described by words. This paper presents a new system which may be used to query for images from the internet which is based on our Query by Approximate Shapes algorithm. The main idea of the proposed approach is to gather images from the internet. Next, all images are processed using our algorithm which is based on decomposing objects into a set of simple shapes. During the query, depending on its type, an example image or a sketch is used. For both types a graph is constructed which is compared with graphs in the database.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. Deniziak, R.S., Michno, T.: Content based image retrieval using query by approximate shape. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 807–816. IEEE, Gdańsk (2016). https://doi.org/10.15439/2016f233

  2. Deniziak, R.S., Michno, T.: New content based image retrieval database structure using query by approximate shapes. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 613–621. IEEE, Prague (2017). https://doi.org/10.15439/2017F457

  3. Deniziak, R.S., Michno, T.: Query by shape for image retrieval from multimedia databases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) Beyond Databases, Architectures and Structures. CCIS, vol. 521, pp. 377–386. Springer, Ustroń (2015). https://doi.org/10.1007/978-3-319-18422-7_33

    Google Scholar 

  4. Deniziak, R.S., Michno, T.: Query-by-shape interface for content based image retrieval. In: 2015 8th International Conference on Human System Interaction (HSI), pp. 108–114. IEEE, Warsaw, June 2015. https://doi.org/10.1109/HSI.2015.7170652

  5. Deniziak, R.S., Michno, T., Krechowicz, A.: The scalable distributed two-layer content based image retrieval data store. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 827–832. IEEE, Łódź (2015). https://doi.org/10.15439/2015F272

  6. Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database-query by visual example. In: [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition, pp. 530–533. IEEE, The Hague (1992). https://doi.org/10.1109/ICPR.1992.201616

  7. Kriegel, H.P., Kroger, P., Kunath, P., Pryakhin, A.: Effective similarity search in multimedia databases using multiple representations. In: 2006 12th International Multi-Media Modelling Conference. IEEE, Beijing (2006). https://doi.org/10.1109/MMMC.2006.1651355

  8. Lalos, C., Doulamis, A., Konstanteli, K., Dellias, P., Varvarigou, T.: An innovative content-based indexing technique with linear response suitable for pervasive environments. In: 2008 International Workshop on Content-Based Multimedia Indexing, pp. 462–469. IEEE, London (2008). https://doi.org/10.1109/CBMI.2008.4564983

  9. Li, C.-Y., Hsu, C.-T.: Image retrieval with relevance feedback based on graph-theoretic region correspondence estimation. IEEE Trans. Multimedia 10(3), 447–456 (2008). https://doi.org/10.1109/tmm.2008.917421

    Article  Google Scholar 

  10. Li, B., Lu, Y., Shen, J.: A semantic tree-based approach for sketch-based 3d model retrieval. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3880–3885. IEEE, Cancun (2016). https://doi.org/10.1109/ICPR.2016.7900240

  11. Mocofan, M., Ermalai, I., Bucos, M., Onita, M., Dragulescu, B.: Supervised tree content based search algorithm for multimedia image databases. In: 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 469–472. IEEE, Timisoara (2011). https://doi.org/10.1109/SACI.2011.5873049

  12. Shih, T.K.: Distributed Multimedia Databases. IGI Global, Hershey (2002)

    Book  Google Scholar 

  13. Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Sci. Programm. 2016, Article ID 5102616 (2016). https://doi.org/10.1155/2016/5102616

    Article  Google Scholar 

  14. Śluzek, A.: Machine vision in food recognition: attempts to enhance CBVIR tools. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016. PTI, Gdańsk (2016). https://doi.org/10.15439/2016f579

  15. Wang, H.H., Mohamad, D., Ismail, N.A.: Approaches, challenges and future direction of image retrieval. J. Comput. 2(6) (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kielce University of Technology, al. Tysiaclecia Panstwa Polskiego 7, 25-314, Kielce, Poland

    Roman Stanisław Deniziak & Tomasz Michno

Authors
  1. Roman Stanisław Deniziak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Michno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomasz Michno .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  2. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Javier Prieto

  3. GECAD - Instituto Superior de Engenharia, Porto, Portugal

    Pedro Faria

  4. Department of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland

    Sławomir Kłos

  5. Computing Science and Artificial Intelligence, Rey Juan Carlos University, Móstoles, Madrid, Spain

    Alberto Fernández

  6. Basque Center for Applied Mathematics, Bilbao, Spain

    Santiago Mazuelas

  7. Basque Center for Applied Mathematics, Universidad de Alcalá, Alcalá de Henares, Spain

    M. Dolores Jiménez-López

  8. Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

    María N. Moreno

  9. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena M. Navarro

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deniziak, R.S., Michno, T. (2019). World Wide Web CBIR Searching Using Query by Approximate Shapes. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_10

Download citation

Publish with us

Policies and ethics

Search

Navigation