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Visual structures for image browsing

Published:03 November 2003Publication History

ABSTRACT

Content-Based Image Retrieval (CBIR) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. Database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. Comparatively little work has been done on designing user interfaces for CBIR systems. This, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. This paper describes an initial effort to fill this gap, combining recent research in CBIR and Information Visualization, studied from a Human-Computer Interface perspective. It presents two visualization techniques based on Spiral and Concentric Rings implemented in a CBIR system to explore query results. The approach is centered on keeping user focus on both the query image, and the most similar retrieved images. Experiments conducted so far suggest that the proposed visualization strategies improves system usability.

References

  1. C. Ahlberg and B. Snheiderman. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. In ACM Conference on Human Factors in Computing Systems, pages 313--317, 479--480, 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. B. Bederson. Photomesa: A Zoomable Image Browser Using Quantum Treemaps and Bubblemaps. In ACM Symposium on User Interface Software and Technology, pages 71--80, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. K. Card, J. D. Mackinlay, and B. Shneiderman. Readings in Information Visualization: Using Vision to Think Morgan Kaufmann Publishers, 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. V. Carlis and J. A. Konstan. Interactive Visualization of Serial Periodic Data. In 11th Annual Symposium on User Interface Software and Technology, Nov. 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. E. H. Chi. A Taxonomy of Visualization Techniques Using the Data State Reference Model. In IEEE Symposium on Information Visualization, pages 69--76, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. VLBD Journal, pages 426--435, 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. T. T. A. Combs and B. B. Bederson. Does Zooming Improve Image Browsing? In Proceedings of the Fourth ACM International Conference on Digital Libraries, pages 130--137, 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Flickner, H. Sawhney, W. Niblack, Q. H. J. Ashley, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by Image and Video Content: the QBIC System. IEEE Computer, 28(9):23--32, Sep 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. G. G. R. J. D. Mackinlay and R. DeLine. Developing Calendar Visualizers for the Information Visualizer. In ACM Symposium on User Interface Software and Technology, pages 109--118, 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. A. Keim. Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics, 8(1):1--8, Jan/March 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. D. MacArthur, C. E. Brodley, A. C. Kak, and L. S. Broderick. Interactive Content-Based Image Retrieval Using Relevance Feedback. Computer Vision and Image Understanding, 88(2):55--75, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. V. E. Ogle and M. Stonebraker. Chabot: Retrieval from Relational Database of Images. IEEE Computer, 28(9):40--48, Sep 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. Rodden, W. Basalaj, D. Sinclair, and K. Wood. Does Organization by Similarity Assist Image Browsing? In ACM Conference on Human Factors in Computing Systems, volume 3, pages 190--197, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Santini, A. Gupta, and R. Jain. Emergent Semantics through Interaction in Image Databases. IEEE Transactions on Knowledge and Data Engineering, 13(3):337--351, May/June 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. B. Shneiderman. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In IEEE Symposium on Visual Languages, pages 336--343, Sep 1996.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1349--1380, December 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Stan and I. K. Sethi. eID: a System for Exploration of Image Databases. Information Processing nad Management, 39:335--365, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Q. Tian, B. Moghaddam, and T. S. Huang. Display Optimization for Image Browsing. In 2nd International Workshop on Multimedia Databases and Image Communications, Sep 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. R. S. Torres. Integrating Image and Spatial Data for Biodiversity Information Management. PhD thesis, Institute of Computing, University of Campinas, 2004. Under development.]]Google ScholarGoogle Scholar
  20. R. S. Torres, A. X. Falcão, and L. F. Costa. Shape Description by Image Foresting Transform. In 14th International Conference on Digital Signal Processing, Santorini, Greece, July 2002.]]Google ScholarGoogle ScholarCross RefCross Ref
  21. R. S. Torres, A. X. Falcão, and L. F. Costa. A Graph-based Approach for Multiscale Shape Analysis. Technical Report IC-0303, Institute of Computing, University of Campinas, January 2003.]]Google ScholarGoogle Scholar
  22. R. S. Torres, E. M. Picado, A. X. Falcão, and L. F. Costa. Effective Image Retrieval by Shape Saliences. In Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI03), São Carlos, São Paulo, Brazil, 12-15 October 2003.]]Google ScholarGoogle ScholarCross RefCross Ref
  23. S. Wang and H. Wang. Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing. Knowledge and Information Systems, 4(1):31--45, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. Weber, M. Alexa, and W. Muller. Visualizing Time-Series on Spirals. In IEEE InfoVis Symposium, pages 7--14, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K.-P. Yee, D. Fisher, R. Dhamija, and M. A. Hearst. Animated Exploration of Dynamic Graphs with Radial Layout. In IEEE Symposium on Information Visualization, pages 43--50, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management
            November 2003
            592 pages
            ISBN:1581137230
            DOI:10.1145/956863

            Copyright © 2003 ACM

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            Publication History

            • Published: 3 November 2003

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