ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval

  • Roberto Tronci
  • Gabriele Murgia
  • Maurizio Pili
  • Luca Piras
  • Giorgio Giacinto
Part of the Studies in Computational Intelligence book series (SCI, volume 439)

Abstract

Nowadays, a very large number of digital image archives is easily produced thanks to the wide diffusion of personal digital cameras and mobile devices with embedded cameras. Thus, personal computers, personal storage units, as well as photo-sharing and social-network websites, are rapidly becoming the repository for thousands, or even billions of images (i.e., more than 100 million photos are uploaded every day on the social site Facebook). As a consequence, there is an increasing need for tools enabling the semantic search, classification, and retrieval of images. The use of meta-data associated to images solves the problems only partially, as the process of assigning reliable meta-data to images is not trivial, is slow, and closely related to whom performed the task. One solution for effective image search and retrieval is to combine content-based analysis with feedbacks from the users. In this chapter we present Image Hunter, a tool that implements a Content Based Image Retrieval (CBIR) engine with a Relevance Feedback mechanism. Thanks to a user friendly interface the tool is especially suited to unskilled users. In addition, the modular structure permits the use of the same core both in web-based and stand alone applications.

Keywords

Image Retrieval Query Image Relevance Feedback Relevant Image Image Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  2. 2.
    Barthel, K.U.: Improved image retrieval using automatic image sorting and semi-automatic generation of image semantics. In: WIAMIS 2008: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 227–230. IEEE Computer Society Press, Washington, DC (2008), doi:http://dx.doi.org/10.1109/WIAMIS.2008.56 CrossRefGoogle Scholar
  3. 3.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: W. Chen, J.F. Naughton, P.A. Bernstein (eds.) SIGMOD Conference, pp. 93–104. ACM (2000), doi:http://doi.acm.org/10.1145/342009.335388,db/conf/sigmod/BreunigKNS00.html
  4. 4.
    Chang, S.F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. IEEE Trans. Circuits Syst. Video Techn.Google Scholar
  5. 5.
    Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Chatzichristofis, S.A., Boutalis, Y.S.: Fcth: Fuzzy color and texture histogram - a low level feature for accurate image retrieval. In: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 191–196. IEEE Computer Society (2008), doi:10.1109/WIAMIS.2008.24Google Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000)Google Scholar
  8. 8.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008), doi:http://doi.acm.org/10.1145/1348246.1348248 CrossRefGoogle Scholar
  9. 9.
    Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retr. 11(2), 77–107 (2008), doi:http://dx.doi.org/10.1007/s10791-007-9039-3 CrossRefGoogle Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Inc., New York (2001)MATHGoogle Scholar
  11. 11.
    Giacinto, G.: A nearest-neighbor approach to relevance feedback in content based image retrieval. In: CIVR 2007: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 456–463. ACM, New York (2007), doi:http://doi.acm.org/10.1145/1282280.1282347 CrossRefGoogle Scholar
  12. 12.
    Giacinto, G., Roli, F.: Bayesian relevance feedback for content-based image retrieval. Pattern Recognition 37(7), 1499–1508 (2004), doi:http://dx.doi.org/10.1016/j.patcog.2004.01.005 MATHCrossRefGoogle Scholar
  13. 13.
    Huang, T., Dagli, C., Rajaram, S., Chang, E., Mandel, M., Poliner, G., Ellis, D.: Active learning for interactive multimedia retrieval. Proceedings of the IEEE 96(4), 648–667 (2008), doi:10.1109/JPROC.2008.916364CrossRefGoogle Scholar
  14. 14.
    Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: Lew, M.S., Bimbo, A.D., Bakker, E.M. (eds.) Multimedia Information Retrieval, pp. 39–43. ACM (2008), doi:http://doi.acm.org/10.1145/1460096.1460104
  15. 15.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006), doi:http://doi.acm.org/10.1145/1126004.1126005
  16. 16.
    Lux, M., Chatzichristofis, S.A.: Lire: lucene image retrieval: an extensible java cbir library. In: MM 2008: Proceeding of the 16th ACM International Conference on Multimedia, pp. 1085–1088. ACM, New York (2008), doi:http://doi.acm.org/10.1145/1459359.1459577 CrossRefGoogle Scholar
  17. 17.
    Rui, Y., Huang, T.S.: Relevance feedback techniques in image retrieval. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 219–258. Springer, London (2001)Google Scholar
  18. 18.
    Segarra, F.M., Leiva, L.A., Paredes, R.: A relevant image search engine with late fusion: mixing the roles of textual and visual descriptors. In: Pu, P., Pazzani, M.J., André, E., Riecken, D. (eds.) IUI, pp. 455–456. ACM (2011)Google Scholar
  19. 19.
    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), http://www.computer.org/tpami/tp2000/i1349abs.html CrossRefGoogle Scholar
  20. 20.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Systems, Man and Cybernetics 8(6), 460–473 (1978), doi:10.1109/TSMC.1978.4309999CrossRefGoogle Scholar
  21. 21.
    Tax, D.M.: One-class classification. Ph.D. thesis, Delft University of Technology, Delft, The Netherlands (2001), doi:http://prlab.tudelft.nl/sites/default/files/thesis.pdf
  22. 22.
    Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proc. of the 9th ACM Intl Conf. on Multimedia, pp. 107–118 (2001), doi:http://doi.acm.org/10.1145/500141.500159
  23. 23.
    Tronci, R., Falqui, L., Piras, L., Giacinto, G.: A study on the evaluation of relevance feedback in multi-tagged image datasets. In: International Symposium on Multimedia, pp. 452–457 (2011), doi:http://doi.ieeecomputersociety.org/10.1109/ISM.2011.80
  24. 24.
    Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001), doi:http://www.computer.org/tpami/tp2001/i0947abs.html CrossRefGoogle Scholar
  25. 25.
    Zhou, X.S., Huang, T.S.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Syst. 8(6), 536–544 (2003), http://www.springerlink.com/openurl.asp?genre=article&issn=0942-4962&volume=8&issue=6&spage=536 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roberto Tronci
    • 1
  • Gabriele Murgia
    • 1
  • Maurizio Pili
    • 1
  • Luca Piras
    • 2
  • Giorgio Giacinto
    • 2
  1. 1.AmILAB - Laboratorio Intelligenza d’AmbienteSardegna RicerchePulaItaly
  2. 2.DIEE - Department of Electric and Electronic EngineeringUniversity of CagliariCagliariItaly

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