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Challenges of Image and Video Retrieval

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Image and Video Retrieval (CIVR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2383))

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Abstract

What use is the sum of human knowledge if nothing can be found? Although significant advances have been made in text searching, only preliminary work has been done in finding images and videos in large digital collections. In fact, if we examine the most frequently used image and video retrieval systems (i.e. www.google.com) we find that they are typically oriented around text searches where manual annotation was already performed.

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References

  1. Wang, W., Song, Y., Zhang, A.: Semantics-based image retrieval by region saliency. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 27–35

    Chapter  Google Scholar 

  2. de Bruijn, W., Lew, M.: Atomsnet: Multimedia peer 2 peer for file sharing. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 130–138

    Chapter  Google Scholar 

  3. Vendrig, J., Worring, M.: Multimodal person identification in movies. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 166–175

    Chapter  Google Scholar 

  4. Schaffalitzky, F., Zisserman, A.: Automated scene matching in movies. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 176–185

    Chapter  Google Scholar 

  5. Denman, H., Rea, N., Kokaram, A.: Content based analysis for video from snooker broadcasts. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 186–193

    Chapter  Google Scholar 

  6. Visser, R., Sebe, N., Bakker, E.: Object recognition for video retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 250–259

    Chapter  Google Scholar 

  7. Bakker, E., Lew, M.: Semantic video retrieval using audio analysis. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 260–267

    Chapter  Google Scholar 

  8. Kim, K., Choi, J., Kim, N., Kim, P.: Extracting semantic information from basketball video based on audio-visual features. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 268–277

    Chapter  Google Scholar 

  9. Ruiz-del-Solar, J., Navarrete, P.: FACERET: An interactive face retrieval system based on self-organizing maps. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 149–156

    Chapter  Google Scholar 

  10. Oh, K., Zaher, A., Kim, P.: Fast k-nn image search with self-organizing maps. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 288–297

    Chapter  Google Scholar 

  11. Hussain, M., Eakins, J., Sexton, G.: Visual clustering of trademarks using the self-organizing map. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 139–148

    Chapter  Google Scholar 

  12. Huang, J., Umamaheswaran, D., Palakal, M.: Video indexing and retrieval for archeological digital library, CLIOH. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 278–287

    Chapter  Google Scholar 

  13. Park, G., Baek, Y., Lee, H. K.: A ranking algorithm using dynamic clustering for content-based image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 316–324

    Chapter  Google Scholar 

  14. Pickering, M., Rüger, S., Sinclair, D.: Video retrieval by feature learning in key frames. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 298–306

    Chapter  Google Scholar 

  15. Smeaton, A.: Challenges for content-based navigation of digital video in the Físchlár digital library. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 203–215

    Chapter  Google Scholar 

  16. Enser, P., Sandom, C.: Retrieval of archival moving imagery-CBIR outside the frame? In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 194–202

    Chapter  Google Scholar 

  17. Burke, M.: Personal construct theory as a research tool for analysing user perceptions of photographs. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 363–370

    Chapter  Google Scholar 

  18. Tian, Q., Moghaddam, B., Huang, T.: Visualization, estimation, and user-modeling for interactive browsing of image libraries. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 7–16

    Chapter  Google Scholar 

  19. Cox, G., de Jager, G.: A linear image-pair model and the associated hypothesis test for matching. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 56–63

    Chapter  Google Scholar 

  20. Orriols, X., Binefa, X.: Online bayesian video summarization and linking. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 325–334

    Chapter  Google Scholar 

  21. Bosson, A., Cawley, G., Chan, Y., Harvey, R.: Non-retrieval: Blocking pornographic images. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 46–55

    Chapter  Google Scholar 

  22. Viallet, J., Bernier, O.: Face detection for video summaries. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 335–342

    Chapter  Google Scholar 

  23. Smolka, B., Plataniotis, K.: On the coupled forward and backward anisotropic diffusion scheme for color image enhancement. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 64–73

    Chapter  Google Scholar 

  24. Ko, B., Byun, H.: Multiple regions and their spatial relationship-based image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 74–83

    Chapter  Google Scholar 

  25. Wolf, J., Burgard, W., Burkhardt, H.: Using an image retrieval system for vision-based mobile robot localization. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 102–111

    Chapter  Google Scholar 

  26. Qiu, G., Lam, K. M.: Spectrally layered color indexing. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 93–101

    Chapter  Google Scholar 

  27. Š. Obdržálek, Matas, J.: Local affine frames for image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 307–315

    Google Scholar 

  28. Sebe, N., Lew, M.: Robust shape matching. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 17–26

    Chapter  Google Scholar 

  29. Leung, M. W., Chan, K. L.: Object-based image retrieval using hierarchical shape descriptor. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 157–165

    Chapter  Google Scholar 

  30. Brucale, A., d’Amico, M., Ferri, M., Gualandri, M., Lovato, A.: Size functions for image retrieval: A demonstrator on randomly generated curves. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 223–232

    Chapter  Google Scholar 

  31. Berens, J., Finlayson, G.: An efficient coding of three dimensional colour distributions for image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 233–240

    Chapter  Google Scholar 

  32. de Alarcón, P., Pascual-Montano, A., Carazo, J.: Spin images and neural networks for efficient content-based retrieval in 3D object databases. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 216–222

    Chapter  Google Scholar 

  33. Feng, G., Jiang, J.: JPEG image retrieval based on features from DCT domain. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 112–120

    Chapter  Google Scholar 

  34. Riley, K., Eakins, J.: Content-based retrieval of historical watermark images: I-Tracings. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 241–249

    Chapter  Google Scholar 

  35. Howell, A., Young, D.: Image retrieval methods for a database of funeral monuments. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 121–129

    Chapter  Google Scholar 

  36. Fauzi, M., Lewis, P.: Query by fax for content-based image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 84–92

    Chapter  Google Scholar 

  37. Müller, H., Marchand-Maillet, S., Pun, T.: The truth about Corel-Evaluation in image retrieval. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 36–45

    Chapter  Google Scholar 

  38. Black, J., Fahmy, G., Panchanathan, S.: A method for evaluating the performance of content-based image retrieval systems based on subjectively determined similarity between images. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 343–352

    Chapter  Google Scholar 

  39. Sebe, N., Tian, Q., Loupias, E., Lew, M., Huang, T.: Evaluation of salient point techniques. In: International Conference on Image and Video Retrieval, Lecture Notes in Computer Science, vol. 2383, Springer (2002) 353–362

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIACS Media Lab, Leiden University, The Netherlands

    Michael S. Lew & Nicu Sebe

  2. Institute for Image Data Research, University of Northumbria at Newcastle, UK

    John P. Eakins

Authors
  1. Michael S. Lew
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  2. Nicu Sebe
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  3. John P. Eakins
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Editor information

Editors and Affiliations

  1. LIACS Media Lab, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

    Michael S. Lew  & Nicu Sebe  & 

  2. Institute for Image Data Research, University of Northumbria, Newcastle, NE1 8ST, UK

    John P. Eakins

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© 2002 Springer-Verlag Berlin Heidelberg

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Lew, M.S., Sebe, N., Eakins, J.P. (2002). Challenges of Image and Video Retrieval. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds) Image and Video Retrieval. CIVR 2002. Lecture Notes in Computer Science, vol 2383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45479-9_1

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  • DOI: https://doi.org/10.1007/3-540-45479-9_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43899-1

  • Online ISBN: 978-3-540-45479-3

  • eBook Packages: Springer Book Archive

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