Interactive components for visual exploration of multimedia archives

ABSTRACT

With the increase of online resources, one main challenge for multimedia content providers is to provide efficient and user friendly tools for a deep and shallow navigation adapted to large scale audiovisual content. This paper describes a generic framework to build visual interactive applications the objectives of which are to enhance the understanding and to allow easy access to multimedia resources and management. Visual Maps are built on multi-modal similarity matrices computed from automatically extracted descriptors and use graph clustering and layout methods. Active relevance feedback methods are applied to allow users to control the maps evolution according to their needs. The First results of users' evaluation are presented for one of our tools.

References

1. J. Law-To, O. Buisson, V. Gouet-Brunet and N. Boujemaa, Robust Voting Algorithm Based on Labels of Behavior for Video Copy Detection, in Proc. of the ACM Multimedia, 2006.
2. N. Boujemaa and J. Fauqueur and M. Ferecatu and F. Fleuret and V. Gouet and B. Le Saux and H. Sahbi", IKONA for interactive specific and generic image retrieval", MMCBIR 2001, France", 2001.
3. N. Grira, M. Crucianu, and N. Boujemaa. Fuzzy clustering with pairwise constraints for knowledge-driven image categorization. In IEEE Proceedings on Vision, Image and Signal Processing, 2006.
4. M. Larson, S. Eickeler, J. Köhler. Supporting radio archive workflows with vocabulary independent spoken keyword search. Searching Spontaneous Conversational Speech Workshop, SIGIR 2007, Amsterdam
5. A. Joly, C. Frélicot and O. Buisson, Feature statistical retrieval applied to content-based copy identification, in Proc. of Int. Conf. on Image Processing, 2004.
6. A. Joly, O. Buisson and C. Frélicot, Content-Based Copy Retrieval Using Distorsion-based Probabilistic Similarity Search, in IEEE Transactions on Multimedia, 2007.
7. S. Poullot, O. Buisson and M. Crucianu, Zgrid-based Probabilistic Retrieval for Scaling Up Content-Based Copy Detection, in Proc. of CIVR, 2007.
8. Jérôme Thièvre PHD Thesis, INA/Univ of Montpellier, 2006
9. I. Herman, G. Melançon, and M. S. Marshall. Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6(1):24--43, 2000.
10. A. Noack. An energy model for visual graph clustering. In G. Liotta, editor, 11th International Symposium on Graph Drawing (GD 2003), LNCS 2912, pages 425--436, Berlin, 2004. Springer-Verlag.
11. T. M. J. Fruchterman and E. M. Rheingold, "Graph drawing by force directed placement," in Software - Practice and Experience, 1991, vol. 21, pp. 1129--1164.
12. A. Noack. Energy-based clustering of graphs with non uniform degrees. In 14th International Symposium on Graph Drawing (GD 2006), pages 309--320, Limerick, Ireland, 2006.
13. H. Goëau and O. Buisson and M-L. Viaud}, Image Collection Structuring Based On Evidential Active Learner, Sixth International Workshop on Content-Based Multimedia Indexing, London, UK, 2008.
14. H. Goëau and J. Thièvre and M-L. Viaud and D. Pellerin, Interactive Visualization Tool with Graphic Table of Video Contents, EEE International Conference on Multimedia and Expo, p 807--810, Beijing, China, 2007.
15. Nielsen J. Usability Engineering. Academic Press, Boston, 1993.
16. Shackel, B. (1991). Usability -- context, framework, design and evaluation. In Shackel, B. and Richardson, S. (eds.). Human Factors for Informatics Usability. Cambridge University Press, Cambridge, 21--38.
17. Shneiderman, B. (1992). Designing the User Interface: Strategies for Effective Human-Computer Interaction. 2nd edition. Reading, MA: Addison Wesley.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision representations

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

      • Relevance assessment

    • Retrieval tasks and goals

      • Clustering and classification

      • Document filtering

      • Information extraction

  • Information systems applications

    • Data mining

      • Clustering