Skip to main content

Mining Large-Scale News Video Database Via Knowledge Visualization

  • Conference paper
Advances in Visual Information Systems (VISUAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4781))

Included in the following conference series:

Abstract

In this paper, a novel framework is proposed to enable intuitive mining and exploration of large-scale video news databases via knowledge visualization. Our framework focuses on two difficult problems: (1) how to extract the most useful knowledge from the large amount of common, uninteresting knowledge of large-scale video news databases, and (2) how to present the knowledge to the users intuitively. To resolve the two problems, the interactive database exploration procedure is modeled at first. Then, optimal visualization scheme and knowledge extraction algorithm are derived from the model. To support the knowledge extraction and visualization, a statistical video analysis algorithm is proposed to extract the semantics from the video reports.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD. vol. 22, pp. 207–216 (1993)

    Google Scholar 

  2. Wise, J.A., Thomas, J.J., Pennock, K., Lantrip, D., Pottier, M., Schur, A., Crow, V.: Visualizing the non-visual: Spatial analysis and interaction with information from text documents. In: IEEE InfoVis, pp. 51–58. IEEE Computer Society Press, Los Alamitos (1995)

    Google Scholar 

  3. Swan, R., Jensen, D.: Timemines: Constructing timelines with statistical models of word. In: ACM SIGKDD, pp. 73–80. ACM Press, New York (2000)

    Google Scholar 

  4. Havre, S., Hetzler, B., Nowell, L.: Themeriver: Visualizing theme changes over time. In: IEEE InfoVis, pp. 115–123. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  5. Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-base image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  6. Fan, J., Luo, H., Elmagarmid, A.K.: Concept-oriented indexing of video database toward more effective retrieval and browsing. IEEE Trans. on Image Processing 13(7), 974–992 (2004) (IF: 2.715. Google Cite: 12. SCI Cite: 6)

    Article  Google Scholar 

  7. Flickner, M., Sawhney, H., Niblack, W., Huang, J.A.Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The qbic system. Computer 28(9), 23–32 (1995)

    Article  Google Scholar 

  8. Fan, J., Gao, Y., Luo, H.: Multi-level annotation of natural scenes using dominant image components and semantic image concepts. In: ACM Multimedia, pp. 540–547. ACM Press, New York (2004) (Best paper runner-up. Accept rate: 17%. Cite: 17)

    Google Scholar 

  9. Dimitrova, N., Zhang, H., Shahraray, B., Sezan, L., Huang, T., Zakhor, A.: Applications of video-content analysis and retrieval. IEEE Trans. on Multimedia 9(3), 42–55 (2002)

    Article  Google Scholar 

  10. van Wijk, J.J.: Bridging the gaps. Computer Graphics and Applications 26(6), 6–9 (2006)

    Article  Google Scholar 

  11. Hauptmann, A.G.: Lessons for the future from a decade of informedia video analysis research. In: Leow, W.-K., Lew, M.S., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, Springer, Heidelberg (2005)

    Google Scholar 

  12. Luo, H., Fan, J., Yang, J., Ribarsky, W., Satoh, S.: Exploring large-scale video news via interactive visualization. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 75–82. IEEE Computer Society Press, Los Alamitos (2006)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Guoping Qiu Clement Leung Xiangyang Xue Robert Laurini

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luo, H., Fan, J., Satoh, S., Xue, X. (2007). Mining Large-Scale News Video Database Via Knowledge Visualization. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76414-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76413-7

  • Online ISBN: 978-3-540-76414-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics