USING ASSOCIATION RULE MINING TO ENRICH SEMANTIC CONCEPTS FOR VIDEO RETRIEVAL

Nastaran Fatemi, Florian Poulin, Laura E. Raileanu, Alan F. Smeaton

Abstract

In order to achieve true content-based information retrieval on video we should analyse and index video with high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, etc. However the range of such high-level semantic concepts, detected either manually or automatically, is usually limited compared to the richness of information content in video and the potential vocabulary of available concepts for indexing. Even though there is work to improve the performance of individual concept classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences are available to us. We describe in this paper our method for using association rule mining to automatically enrich the representation of video content through a set of semantic concepts based on concept co-occurrence patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach.

References

  1. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases (VLDB), pages 487-499. MorganKaufmann.
  2. Dasiopoulou, S., I.Kompatsiaris, and M.G.Strintzis (2008). Using fuzzy dls to enhance semantic image analysis. In SAMT'08: 3rd International Conference on Semantic and Digital Media Technologies.
  3. Dimitrova, N., Agnihotri, L., Jasinschi, R., Zimmerman, J., Marmaropoulos, G., McGee, T., and Dagtas, S. (2000). Video scouting demonstration: smart content selection and recording. In ACM Multimedia, pages 499-500.
  4. Ebadollahi, S., Xie, L., Chang, S.-F., and Smith, J. R. (2006). Visual event detection using multidimensional concept dynamics. In IEEE International Conference on Multimedia and Expo (ICME 06), Toronto.
  5. Fatemi, N., Raileanu, L., and Poulin, F. (2007). LSVAM - Large Scale Video Annotation Mining: Second part. Internal Report IICT-COM/2007-3.
  6. Garnaud, E., Smeaton, A. F., and Koskela., M. (2006). Evaluation of a video annotation tool based on the lscom ontology. In SAMT 2006: Proceedings of The First International Conference on Semantics And Digital Media Technology, pages 35-36.
  7. Han, J., Pei, J., and Yin, Y. (2000). Mining frequent patterns without candidate generation. In Chen, W., Naughton, J., and Bernstein, P. A., editors, 2000 ACM SIGMOD Intl. Conference on Management of Data, pages 1-12. ACM Press.
  8. Hauptmann, A., Yan, R., and Lin, W.-H. (2007). How many high-level concepts will fill the semantic gap in news video retrieval? In CIVR 7807: Proceedings of the 6th ACM international conference on Image and video retrieval, pages 627-634.
  9. Ken-Hao, L., Ming-Fang, W., Chi-Yao, T., Yung-Yu, C., and Ming-Syan, C. (2008). Association and temporal rule mining for post-processing of semantic concept detection in video. IEEE Transactions on Multimedia, special issue on Multimedia Data Mining, 10(2):240- 251.
  10. Koskela, M. and Smeaton, A. F. (2006). Clustering-based analysis of semantic concept models for video shots. In ICME, pages 45-48.
  11. Lin, W.-H. and Hauptmann, A. (July 2006). Which thousand words are worth a picture? experiments on video retrieval using a thousand concepts. Multimedia and Expo, 2006 IEEE International Conference on, pages 41-44.
  12. Naphade, M., Smith, J. R., Tesic, J., Chang, S.-F., Hsu, W., Kennedy, L., Hauptmann, A., and Curtis, J. (2006). Large-Scale Concept Ontology for Multimedia. IEEE MultiMedia, 13(3):86-91.
  13. Over, P., Ianeva, T., Kraaij, W., and Smeaton, A. F. (2005a). TRECVID 2005 - An Overview. In TRECVid 2005: Proceedings of the TRECVID Workshop, Md., USA. National Institute of Standards and Technology.
  14. Over, P., Ianeva, T., Kraaij, W., and Smeaton, A. F. (2005b). Trecvid 2005 - an overview. In Proceedings of TRECVID 2005.
  15. Pack, S. and Chang, S.-F. (2000). Experiments in constructing belief networks for image classification systems. In Proceedings of the International Conference on Image Processing.
  16. Smeaton, A. F. (2007). Techniques used and open challenges to the analysis, indexing and retrieval of digital video. Information Systems Journal, 32(4):545-559.
  17. Volkmer, T., Smith, J., and Natsev, A. (2005). A web-based system for collaborative annotation of large image and video collections: an evaluation and user study. Proceedings of the 13th annual ACM international conference on Multimedia, pages 892-901.
  18. Xie, L. and Chang, S.-F. (2006). Pattern mining in visual concept streams. In IEEE International Conference on Multimedia and Expo (ICME 06), Toronto.
  19. Yan, R., yu Chen, M., and Hauptmann, A. G. (2006). Mining relationship between video concepts using probabilistic graphical models. In ICME, pages 301-304.
  20. Zha, Z.-J., Mei, T., Hua, X.-S., Qi, G.-J., and Wang, Z. (2007). Refining video annotation by exploiting pairwise concurrent relation. In MULTIMEDIA 7807: Proceedings of the 15th international conference on Multimedia, pages 345-348.
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Paper Citation


in Harvard Style

Fatemi N., Poulin F., E. Raileanu L. and F. Smeaton A. (2009). USING ASSOCIATION RULE MINING TO ENRICH SEMANTIC CONCEPTS FOR VIDEO RETRIEVAL . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 119-126. DOI: 10.5220/0002275701190126


in Bibtex Style

@conference{kdir09,
author={Nastaran Fatemi and Florian Poulin and Laura E. Raileanu and Alan F. Smeaton},
title={USING ASSOCIATION RULE MINING TO ENRICH SEMANTIC CONCEPTS FOR VIDEO RETRIEVAL},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={119-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002275701190126},
isbn={978-989-674-011-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - USING ASSOCIATION RULE MINING TO ENRICH SEMANTIC CONCEPTS FOR VIDEO RETRIEVAL
SN - 978-989-674-011-5
AU - Fatemi N.
AU - Poulin F.
AU - E. Raileanu L.
AU - F. Smeaton A.
PY - 2009
SP - 119
EP - 126
DO - 10.5220/0002275701190126