Advertisement

Region Based Semantic Image Retrieval Using Ontology

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

Extracting Semantic images from the large amount of heterogeneous image data is a quiet challenge in Content Based Image Retrieval (CBIR). Search space and Semantic gap reduction are two major issues in extracting semantic images. The proposed method of Region based semantic image retrieval considers both Search space and Semantic gap reduction. The proposed methodology first does the region based clustering as it reduces retrieval search space. Later it reduces the semantic gap with the support of ontology framework. The ontology framework shares the information among image seekers and domains. Our experimental results reveal the efficacy of the proposed method.

Keywords

CBIR Ontology Search space Semantic gap Semantic image retrieval 

References

  1. 1.
    M.R. Naphade and T.S. Huang, Extracting semantics from audio-visual content: the final frontier in multimedia retrieval, IEEE Trans. on Neural Networks, vol. 13, no. 4, pp. 793–810(2002).Google Scholar
  2. 2.
    A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, Content-based image retrieval at the end of the early years, IEEE Trans. Pattern Anal. Mach. Intell. 22 (12) 1349–1380(2000).Google Scholar
  3. 3.
    X.S. Zhou, T.S. Huang, CBIR: from low-level features to high level semantics, in: Proceedings of the SPIE, Image and Video Communication and Processing, vol. 3974, San Jose, CA, pp. 426–431(2000).Google Scholar
  4. 4.
    Ying Liu, XIN Chen, Chengcui Zhang, and Alan Sprague, Semantic Clustering for region based Image Retrieval, Ninth IEEE International Symposium on Multimedia, pp. 167–172(2007).Google Scholar
  5. 5.
    J. Li, N.M. Allinson, Relevance feedback in content-based image retrieval: a survey, in: Handbook on Neural Information Processing, Springer, Berlin Heidelberg, pp. 433–469(2013).Google Scholar
  6. 6.
    Roung Shiunn Wu, Wen Hsien Hsu, A Semantic Image Retrieval Frame work based on Ontology and Naïve Bayesian Inference, International Journal of Multimedia Technology, p. 36–43(2012).Google Scholar
  7. 7.
    J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N.Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer et al., Dbpedia–a large-scale, multilingual knowledge base extracted fromwikipedia, Semantic Web Journal, (2013).Google Scholar
  8. 8.
    RC. Veltkamp, M. Tanase, Content-Based Image Retrieval Systems: A Survey, rapport no UU-CS-2000-34, (2000).Google Scholar
  9. 9.
    R. Datta, J. Li, JZ. Wang, Content-based image retrieval: approaches and trends of the new age, in: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262(2005).Google Scholar
  10. 10.
    M.S. Lew, N. Sebe, C. Djeraba, R. Jain, Content-based multimedia information retrieval: state of the art and challenges, ACM Trans. Multim. Comput., Commun., pp. 1–19(2006).Google Scholar
  11. 11.
    R. Datta, D. Joshi, J. Li, J. Wang, Image retrieval: ideas, influences, and trends of the new age, ACM Comput. Surv. (CSUR) 40 (2)(2008).Google Scholar
  12. 12.
    R. Brunelli, O. Mich, Image retrieval by examples, IEEE Trans. Multim. 2 (3), pp. 164–171(2000).Google Scholar
  13. 13.
    C.M. Bishop, Pattern Recognition and Machine Learning, Springer, (2006).Google Scholar
  14. 14.
    W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Pektovic, P. Yanker, C. Faloutsos, G. Taubin, The QBIC project: Querying images by content using color, texture and shape, in: Proceedings of the SPIE Storage and Retrieval for Image and Video Databases, San Jose, CA, (1994).Google Scholar
  15. 15.
    J.R. Smith, S.F. Chang, VisualSEEk: a fully automated content-based image query system, in: Proceedings of the Forth ACM International Conference on Multimedia ‘96, Boston, MA, (1996).Google Scholar
  16. 16.
    J.Z. Wang, J. Li, G. Wiederhold, SIMPLIcity: semantics-sensitive integrated matching for picture libraries, IEEE Trans. Pattern Anal. Mach. Intell., pp. 947–963(2001).Google Scholar
  17. 17.
    B.S. Manjunath and W.Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837–842(1996).Google Scholar
  18. 18.
    W.Y. Ma and B.S. Manjunath, “NeTra: A Toolbox for Navigating Large Image Databases,” Multimedia Systems, vol. 7, no. 3, pp. 184–198(1999).Google Scholar
  19. 19.
    T.S. Huang, S. Mehrotra, and K. Ramchandran, “Multimedia analysis and retrieval system (mars) project,” in Proc of 33rd Annual Clinic on Library Application of Data Processing—Digital Image Access and Retrieval (1996).Google Scholar
  20. 20.
    A. Pentland, R. Picard, and S. Sclaroff, “Photobook: Content-based manipulation of image databases,” Int. J. Computer Vision, vol. 18, no. 3, pp. 233–254(1996).Google Scholar
  21. 21.
    J.R. Smith and S.-F. Chang, “Visualseek: A fully automated content-based image query system,” in ACM Multimedia, pp. 87–98(1996).Google Scholar
  22. 22.
    I. Kompatsiaris, E. Triantafillou, and M. G. Strintzis, “Region-Based Color Image Indexing and Retrieval,” in Proc. IEEE International Conference on Image Processing, Thessaloniki, Greece, (2001).Google Scholar
  23. 23.
    J.R. Smith, S.F. Chang, Visually searching the Web for content, IEEE Multim., pp. 12–20(1997).Google Scholar
  24. 24.
    C. Carson, S. Belongie, H. Greenspan, J. Malik, Blobworld: image segmentation using expectation-maximization and its application to image querying, IEEE Trans. Pattern Anal. Mach. Intell., 1026–1038(2002).Google Scholar
  25. 25.
    S. Sclaroff, M. LaCascia, S. Sethi, L. Taycher, Unifying textual and visual cues for content-based image retrieval on the World Wide Web, Comp. Vis. Image Understand, pp. 86–98(1999).Google Scholar
  26. 26.
    Ning RUAN, Ning HUANG, Wen Hong, Semantic based image retrieval in remote sensing archieve: An Ontology approach, IEEE, pp. 2888–2891(2006).Google Scholar
  27. 27.
    Sohail Sarwara, Zia Ul Qayyum, Saqib Majeed, Ontology Based Image Retrieval Framework using Qualitative Semantic Image Descriptions, ICKBIIES, Elsevier, pp. 285–294(2013).Google Scholar
  28. 28.
    Anuja Khodaskar, Siddarth Ladhake, New-Fangled Alignment of Ontologies for Content based Semantic Image Retrieval, ICCC, Elsevier, pp. 298–303(2015).Google Scholar
  29. 29.
    Madiha Liaqat, Sharifullah Khan, Muhammad Majid, Fuzzy Ontology based Model for Image Retrieval, Mobile web and Intelligent Information Systems, LNCS, Vol. 9847, pp. 108–120, Springer (2016).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.CMR Institute of TechnologyHyderabadIndia
  2. 2.JNTUH College of Engineering SultanpurSultanpurIndia

Personalised recommendations