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Interactive Experiments in Object-Based Retrieval

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Image and Video Retrieval (CIVR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4071))

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Abstract

Object-based retrieval is a modality for video retrieval based on segmenting objects from video and allowing end-users to use these objects as part of querying. In this paper we describe an empirical TRECVid-like evaluation of object-based search, and compare it with a standard image-based search into an interactive experiment with 24 search topics and 16 users each performing 12 search tasks on 50 hours of rushes video. This experiment attempts to measure the impact of object-based search on a corpus of video where textual annotation is not available.

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References

  1. Oomoto, E., Tanaka, K.: Ovid: Design and implementation of a video-object database system. IEEE Transactions on Knowledge and Data Engineering, 5(4) (1993)

    Google Scholar 

  2. Smeaton, A.F., Browne, P.: A Usage Study of Retrieval Modalities for Video Shot Retrieval. Information Processing and Management (in press, 2006)

    Google Scholar 

  3. Hauptmann, A., Christel, M.: Successful Approaches in the TREC Video Retrieval Evaluations. In: Proceedings of ACM Multimedia (2004)

    Google Scholar 

  4. Sav, S., Lee, H., Smeaton, A.F., O’Connor, N.E., Murphy, N.: Using Video Objects and Relevance Feedback in Video Retrieval. In: Proceedings of the SPIE Conference on Multimedia Systems and Applications VIII, Boston, Mass (November 2005)

    Google Scholar 

  5. TRECVid Evaluation, available at: http://www-nlpir.nist.gov/projects/trecvid

  6. Hohl, L., Souvannavong, F., Merialdo, B., Huet, B.: Enhancing latent semantic analysis video object retrieval with structural information. In: ICIP 2004 - International Conference on Image Processing (2004)

    Google Scholar 

  7. Erol, B., Kossentini, F.: Shape-based retrieval of video objects. IEEE Transactions on Multimedia, 7(1) (2005)

    Google Scholar 

  8. Sivic, J., Shaffalitzky, F., Zisserman, A.: Efficient object retrieval from videos. In: EUSIPCO 2004 - European Signal Processing Conference (2004)

    Google Scholar 

  9. Liu, C.-B., Ahuja, N.: Motion based retrieval of dynamic objects in videos. In: Proceedings of ACM Multimedia (2004)

    Google Scholar 

  10. Smith, M., Khotanzad, A.: An object-based approach for digital video retrieval. In: ITCC 2004 - International Conference on Information Technology: Coding and Computing (2004)

    Google Scholar 

  11. Browne, P., Gurrin, C., Lee, H., McDonald, K., Sav, S., Smeaton, A.F., Ye, J.: Dublin City University Video Track Experiments for TREC 2001. In: TREC 2001 - Proceedings of the Text REtrieval Conference (2001)

    Google Scholar 

  12. Adamek, T., O’Connor, N.E.: A Multiscale Representation Method for Nonrigid Shapes With a Single Closed Contour. IEEE Transactions on Circuits and Systems for Video Technology 14(5) (May 2004)

    Google Scholar 

  13. Tuncel, E., Onural, L.: Utilization of the recursive shortest spanning tree algorithm for video-object segmentation by 2D affine motion modelling. IEEE Transactions on Circuits and Systems for Video Technology 10(5) (August 2000)

    Google Scholar 

  14. MPEG-7(xm) version 10.0, ISO/IEC/JTC1/SC29/WG11, N4062 (2001)

    Google Scholar 

  15. O’Connor, N.E., Cooke, E., LeBorgne, H., Blighe, M., Adamek, T.: The AceToolbox: Low-Level Audiovisual Feature Extraction for Retrieval and Classification. In: IEE European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, London, UK (2005)

    Google Scholar 

  16. The AceMedia project, available at: http://www.acemedia.org

  17. Manjunath, B., Salembier, P., Sikora, T.: Introduction to MEPG: Multimedia Content Description Standard. Wiley, New York (2001)

    Google Scholar 

  18. The Google image search page, available at: http://images.google.com

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© 2006 Springer-Verlag Berlin Heidelberg

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Sav, S., Jones, G.J.F., Lee, H., O’Connor, N.E., Smeaton, A.F. (2006). Interactive Experiments in Object-Based Retrieval. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_1

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  • DOI: https://doi.org/10.1007/11788034_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36018-6

  • Online ISBN: 978-3-540-36019-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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