Majority based ranking approach in web image retrieval

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2728, Issue , 2003, Pages 111-120

  Park, Gunhan ^a   Baek, Yunju ^a   Lee, Heung Kyu ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT BASED RETRIEVAL; IMAGE ANALYSIS; INFORMATION RETRIEVAL; SEARCH ENGINES;

CONVENTIONAL SYSTEMS; KEYWORD-BASED RETRIEVAL; PRECISION AND RECALL; RANKING APPROACH; RANKING PROBLEMS; RETRIEVAL PERFORMANCE; RETRIEVED IMAGES; WEB IMAGE RETRIEVAL;

IMAGE RETRIEVAL;

EID: 35248816533     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45113-7_12     Document Type: Article

References (14)

1. M. J. Swain, and D.H. Ballad, "Color Indexing," International Journal of Computer Vision, Vol. 7, No. 1, pp. 11-32, 1991.
2. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W Niblack, D. Petkovic, and W. Equitz, "Efficient and Effective Querying by Image Content," Journal of Intelligent Information Systems, Vol. 3, No. 3/4, pp. 231-262, 1994.
3. J. R. Smith, "Integrated Spatial and Feature Image Systems : Retrieval, Analysis and Compression," Doctoral Dissertations, Columbia University, 1997.
4. W. Y. Ma and B. S. Manjunath, "NeTra: a toolbox for navigating large image databases", Multimedia Systems, Vol. 7, No. 3, pp. 184-198, 1999.
5. J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries,'" IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 9, pp. 947-963, 2001.
6. C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp. 1026-1038, 2002.
7. W. B. Frakes, and R. Baeza-Yates, "Information Retrieval : Data Structures & Algorithms," Prentice Hall, 1992.
8. S. sclaroff, M. la Cascia, S. Sethi, and L. Taycher, "Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web," Computer Vision and Image Understanding, Vol. 75, No.1/2, pp. 86-98, 1999.
9. T. Gevers and A. W. M. Smeulders, "Pictoseek: A content-based image search engine for the WWW," Proceedings of International Conf. On Visual Information Systems, pp. 93-100, 1997.
10. C. Frankel, M. J. Swain, and V. Athitsos, "WebSeer : An Image Search Engine for the World Wide Web," University of Chicago Technical Report TR-96-14, 1996.
11. A. Tombros, R. Villa, R., and C. J. Van Rijsbergen, "The effectiveness of query-specific hierarchic clustering in information retrieval," Information Processing & Management, Vol. 38, No. 4, pp. 559-582, 2002.
12. G. Park, Y Baek, and H. K. Lee, "A Ranking Algorithm Using Dynamic Clustering for Content-Based Image Retrieval,", the Challenge of Image and Video Retrieval(CIVR2002): International Conference on Image and Video Retrieval, pp. 316-324, 2002.
13. E. M. Voorhees, "The cluster hypothesis revisited," Proceedings of 8th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 188-196, 1985.
14. P. Willett, "Recent trends in hierarchic document clustering : A critical review," Information Processing & Management, Vol. 24, No. 5, pp. 577-587, 1988.