Image Navigation: A Massively Interactive Model for Similarity Retrieval of Images

International Journal of Computer Vision

Volumn 56, Issue 1-2, 2004, Pages 131-145

  Lee, Jaeyeon ^a   Jeong, Se Yoon ^a   Han, Kyu Seo ^a   Chun, Byung Tae ^a   Bae, Younglae J ^b  

^a Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

^b Chungbuk National University   (South Korea)

Author keywords

Content based image retrieval; Multi dimensional feature indexing; Query description; Similarity retrieval; Subjectivity problem; User system interaction

Indexed keywords

ALGORITHMS; FEEDBACK; HUMAN COMPUTER INTERACTION; HUMAN ENGINEERING; INDEXING (OF INFORMATION); INTERACTIVE COMPUTER SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING; QUERY LANGUAGES; SEARCH ENGINES; USER INTERFACES;

CONTENT-BASED IMAGE RETRIEVAL; MULTI-DIMENSIONAL FEATURE INDEXING; QUERY DESCRIPTION; SIMILARITY RETRIEVAL; SUBJECTIVITY PROBLEM; USER-SYSTEM INTERACTIONS;

CONTENT BASED RETRIEVAL;

EID: 1042266026     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:VISI.0000004835.55771.0e     Document Type: Article

References (29)

1. Ashwin, T.V., Jain, N., and Ghosal, S. 2001. Improving image retrieval performance with negative relevance feedback. IEEE International Conference on Acoustics, Speech, and Signal Processing, Piscataway, NJ, USA, vol. 3, pp. 1637-1640.
2. Berchtold, S., Bohm, C., and Kriegel, H. 1998. The pyramid-technique: Towards breaking the curse of dimensionality. In International Conference on Management of Data, SCM SIGMOD, USA, pp. 142-153.
3. Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., and Yianilos, P.N. 2000. The bayesian image retrieval system, picHunter: Theory, implementation, and psychophysical experiments. IEEE Trans. on Image Processing, 9(1):20-37.
4. Eakins, J.P. 2002. Towards intelligent image retrieval. Pattern Recognition, 35(1):3-14.
5. Eickeler, S., Wallhoff, F., Iurgel, U., and Rigoll, G. 2001. Content-based indexing of images and video using face detection and recognition methods. In IEEE International Conference on Acoustics, Speech, and Signal Processing, Piscataway, NJ, USA, Proceedings, vol. 3, pp. 1505-1508.
6. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P. 1995. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23-31.
7. Frakes, W.B. 1992. Information Retrieval: Data Structures & Algorithms. Prentice Hall, pp. 28-82.
8. Gevers, T. and Smeulders, A.W.M. 2000. PicToSeek: Combining color and shape invariant features for image retrieval. IEEE Trans. on Image Processing, 9(1):102-119.
9. Gregory, L. and Kittler, J. 2001. Using contextual information for image retrieval. In 11th International Conference on Image Analysis and Processing, Los Alamitos, CA, USA, pp. 230-235.
10. Gudivada, V.N. and Raghavan, V.V. 1995. Content based image retrieval systems. IEEE Computer, 28(9):18-22.
11. Jin, J.S. and Kurniawati, R. 2001. Using browsing to improve content-based image retrieval. Journal of Visual Communication and Image Representation, 12(2):123-135.
12. Jin, Z., King, I., and Li, X. 2000. Content-based image retrieval by relevance feedback. Advances in Visual Information Systems 4th International Conference, VISUAL 2000, Berlin, Germany, pp. 521-529.
13. Klette, R. and Zamperoni, P. 1995. Handbook of Image Processing Operators. John Wiley & Sons: Chichester, England.
14. Laaksonen, J., Koskela, M., Laakso, S., and Oja, E. 2001. Self-organizing maps as a relevance feedback technique in content-based image retrieval. Pattern Analysis & Applications, 4(2/3):140-152.
15. Lee, J.Y., Jeong, S.Y., Kim, K.H., Chun, B.T., and Bae, Y.J. 1999. A fast retrieval method for image features. IEEE TENCON 99, vol. 1, pp. 577-580.
16. Ma, W. and Manjunath, B.S. 1999. NeTra: A toolbox for navigating large image databases. Multimedia Systems, 7(3):184-198.
17. MPEG-7, 2001. Text of ISO/IEC 15938-3/FCD Information Technology - Multimedia Content Description Interface - Part 3 Visual. ISO/IEC JTC1/SC29/WG11/N4062.
18. Muller, H., Muller, W., Squire, D.M., Marchand-Maillet, S., and Pun, T. 2001. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters, 22(5):593-601.
19. Ogle, V.E. and Stonebraker, M. 1995. Chabot: Retrieval from a relational database of images. IEEE Computer, 28(9):40-48.
20. Pecenovic, Z., Do, M.N., Vetterli, M., and Pu, P. 2000. Integrated browsing and searching of large image collections. In Advances in Visual Information Systems 4th International Conference, VISUAL 2000, Berlin, Germany, pp. 279-289.
21. Picard, R.W. and Minka, T.P. 1995. Vision texture for annotation. Multimedia Systems, 3(1):3-14.
22. Rui, Y., Huang, T.S., and Chang, S.-F. 1999. Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication & Image Representation, 10(1):39-62.
23. Santini, S. and Jain, R. 1999. Similarity Measures. IEEE Trans. On Pattern Analysis and Machine Intelligence, 21(9):871-883.
24. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis & Machine Intelligence, 22(12):1349-1380.
25. Srihari, R.K. 1995. Automatic indexing and content-based retrieval of captioned images. IEEE Computer, 28(9):49-56.
26. Stricker, M. and Orengo, M. 1995. Similarity of color images. In Proc. of SPIE: Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 381-392.
27. Tamura, H., Mori, S., and Yamawaki, T. Texture features corresponding to visual perception. IEEE Trans. on Systems, Man, and Cybernetics, 8(6):460-473.
28. Vendrig, J., Worring, M., and Smeulders, A.W.M. 2001. Filter image browsing: Interactive image retrieval by using database overviews. Multimedia Tools and Applications, 15(1):83-103.
29. Weber, R., Schek, H.-J., and Blott, S. 1998. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In 24th International Conference on Very-Large Databases, San Francisco, CA, USA, pp. 194-205.