Visualseek: a fully automated content-based image query system

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Reference

Smith, J., Chang, S.F. (1996). Visualseek: a fully automated content-based image query system. In: Proc. ACM-MM, 87–98.

DOI

http://dx.doi.org/10.1145/244130.244151

Abstract

We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system nds the images that contain the most similar arrangements of similar regions. Prior to the queries, the system automatically extracts and in- dexes salient color regions from the images. By utilizing e cient indexing techniques for color information, re- gion sizes and absolute and relative spatial locations, a wide variety of complex joint color/spatial queries may be computed.

Extended Abstract

Bibtex

Used References

1. M. J. Swain and D. H. Ballard. Color indexing. In- ternational Journal of Computer Vision, 7:1 1991.

2. M. Stricker and A. Dimai. Color indexing with weak spatial constraints. In Symposium on Elec- tronic Imaging: Science and Technology { Stor- age & Retrieval for Image and Video Databases IV, pages 29 - 41. IS&T/SPIE, 1996.

3. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, and C. Falout- sos. The QBIC project: Querying images by con- tent using color, texture, and shape. In Storage and Retrieval for Image and Video Databases, volume SPIE Vol. 1908, February 1993.

4. E. G. M. Petrakis and C. Faloutsos. Similarity searching in large image databases. Technical Re- port 3388, Department of Computer Science, Uni- versity of Maryland, 1995.

5. J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack. E cient color histogram indexing for quadratic form distance functions. IEEE Trans. Pattern Anal. Machine Intell., July 1995.

6. S.-K. Chang, Q. Y. Shi, and C. Y. Yan. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Machine Intell., 9(3):413 { 428, May 1987.

7. H. Samet. The quadtree and related hierarchi- cal data structures. ACM Computing Surveys, 16(2):187 { 260, 1984.

8. V. N. Gudivada and V. V. Raghavan. Design and evaluation of algorithms for image retrieval by spa- tial similarity. ACM Trans. on Information Sys- tems, 13(2), April 1995.

9. A. So er and H. Samet. Retrieveal by content in symbolic-image databases. In Symposium on Elec- tronic Imaging: Science and Technology { Storage & Retrieval for Image and Video Databases IV, pages 144 { 155. IS&T/SPIE, 1996.

10. J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. C. Jain, and C. Shu. Virage image search engine: an open framework for image management. In Symposium on Electronic Imaging: Science and Technology { Storage & Re- trieval for Image and Video Databases IV, pages 76 - 87. IS&T/SPIE, 1996

11. G. Pass, R. Zabih, and J. Miller. Comparing im- ages using color coherence vectors. In Proc. ACM Intern. Conf. Multimedia, Boston, MA, 1996.

12. C. E. Jacobs, A. Finkelstein, and D. H. Salesin. Fast multiresolution image querying. In ACM SIG- GRAPH, Computer Graphics Proceedings, Annual Conference Series, pages 277 { 286, Los Angeles, CA, 1995.

13. J. R. Smith and S.-F. Chang. Tools and techniques for color image retrieval. In Symposium on Elec- tronic Imaging: Science and Technology { Stor- age & Retrieval for Image and Video Databases IV, volume 2670, San Jose, CA, February 1996. IS&T/SPIE.

14. J. R. Smith and S.-F. Chang. Local color and tex- ture extraction and spatial query. In Proc. Int. Conf. Image Processing, Lausanne, Switzerland, September 1996. IEEE.

15. S.-F. Chang. Compressed-domain techniques for image/video indexing and manipulation. In Pro- ceedings, I.E.E.E. International Conference on Im- age Processing, Washington, DC, Oct. 1995. invited paper to the special session on Digital Library and Video on Demand.

16. J. R. Smith and S.-F. Chang. Searching for im- ages and videos on the World-Wide Web. Technical Report CU/CTR 459-96-25, Columbia University, August 1996.

17. A. Guttman. R-trees: A dynamic index structure for spatial searching. In ACM Proc. Int. Conf. Manag. Data (SIGMOD), pages 47 { 57, June 1984.

18. S.-K. Chang. Principles of Pictorial Information Systems Design. Prentice Hall, 1989.


Links

Full Text

http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.45.884&rep=rep1&type=pdf

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Sonstige Links

http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.884

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