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Multimodal Image Retrieval Based on Keywords and Low-Level Image Features

  • Miran Pobar
  • Marina Ivašić-Kos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Image retrieval approaches dealing with the complex problem of image search and retrieval in very large image datasets proposed so far can be roughly divided into those that use text descriptions of images (text-based image retrieval) and those that compare visual image content (content-based image retrieval). Both approaches have their strengths and drawbacks especially in the case of searching for images in general unconstrained domain. To take advantage of both approaches, we propose a multimodal framework that uses both keywords and visual properties of images. Keywords are used to determine the semantics of the query while the example image presents the visual impression (perceptual and structural information) that retrieved images should suit. In the paper, the overview of the proposed multimodal image retrieval framework is presented. For computing the content-based similarity between images different feature sets and metrics were tested. The procedure is described with Corel and Flickr images from the domain of outdoor scenes.

Keywords

Image retrieval Multimodal query Content-based similarity 

References

  1. 1.
    Eakins, J., Graham, M.: Content-based image retrieval. Technical report JTAP-039, JISC, Institute for Image Data Research, University of Northumbria, Newcastle (2000)Google Scholar
  2. 2.
    Hare, J.S., Lewis, P.H., Enser, P.G.B., Sandom, C.J.: Mind the gap: another look at the problem of the semantic gap in image retrieval. In: Multimedia Content Analysis, Management and Retrieval. IS&T/SPIE, Bellingham (2006)Google Scholar
  3. 3.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  4. 4.
    Datta, R., Joshi, D., Li, J.: Image retrieval: ideas, influences, and trends of the new age. ACM Trans. Comput. Surv. 20, 1–60 (2008)Google Scholar
  5. 5.
    Siddiquie, B., White, B., Sharma, A., Davis, L.S.: Multi-modal image retrieval for complex queries using small codes. In: Proceedings of International Conference on Multimedia Retrieval, p. 321. ACM (2014)Google Scholar
  6. 6.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
  8. 8.
    Cha, S.H., Srihari, S.N.: On measuring the distance between histograms. Pattern Recogn. 35(6), 1355–1370 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)CrossRefGoogle Scholar
  10. 10.
    Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of the 4th ACM International Conference on Multimedia, pp. 65–73. ACM (1997)Google Scholar
  11. 11.
    Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of RijekaRijekaCroatia

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