Incorporate feature space transformation to content-based image retrieval with relevance feedback

International Journal of Innovative Computing, Information and Control

Volumn 3, Issue 5, 2007, Pages 1237-1250

  Luo, Xin ^a   Shishibori, Masami ^a   Ren, Fuji ^a,b   Kita, Kenji ^a  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

CBIR; Feature space transformation; Image retrieval; Relevance feedback

Indexed keywords

EID: 63649127226     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. Ma, W. Y. and H. J. Zhang, Content-based image indexing and retrieval, in The Handbook of Multimedia Computing, B. Furht (editor), Boca Raton, 1998.
2. Rocchio, J. J., Relevance feedback in information retrieval, The SMART Retrieval System-Experiments in Automatic Document Processing, Prentice Hall Inc., Englewood Cliffs, NJ, pp.313-323, 1971.
3. Rui, Y., T. S. Huang and S. Mehrotra, Content-based image retrieval with relevance feedback in MARS, Proc. of the IEEE International Conference on Image Processing, vol.2, pp.815-818, 1997.
4. Rui, Y. and T. S. Huang, A novel relevance feedback technique in image retrieval, Proc. of the ACM Multimedia (2), pp.67-70, 1999.
5. Nakajima, S., S. Kinoshita and K. Tanaka, Image database retrieval based on relevance feedback by difference amplification, Transaction IEICE Japan, vol.J87-D-I, no.2, pp.164-174, 2004.
6. Yul, H. and T. Chen, Feature space warping an approach to relevance feedback, ICIP, Rochester, NY, 2002.
7. Gray, R. S., Content-based image retrieval color and edges, Technical Report TR95-252, Dartmouth Institute for Advanced Graduate Studies, 1995.
8. Lu, Z. M., S. Z. Li and H. Burkhardt, A content-based image retrieval scheme in JPEG compressed domain, International Journal of Innovative Computing, Information and Control, vol.2, no.4, pp.831-839, 2006.
9. Zheng, W. M., Z. M. Lu and H. Burkhardt, Color image retrieval schemes using index histograms based on various spatial-domain vector quantizers, International Journal of Innovative Computing, Information and Control, vol.2, no.6, pp.1317-1326, 2006.
10. http : //anipeg.yks.ne.jp/'color.html
11. Michael, O. and S. Mehrotra, Relevance feedback in multimedia databases, Handbook of Video Databases: Design and Applications, CRC Press, 2003.
12. Faloutsos, C, R. Barber, M. Flickner, J. Hafner, W. Niblackm, D. Petkovic and W. Equitz, Efficient and effective querying by image content, Journal of Intelligent Information Systems, vol.3, pp.231-262, 1994.
13. Tamura, H., S. Mori and T. Yamawaki, Texture features corresponding to visual perception, IEEE Trans. Systems Man Cybernetics, vol.8, no.6, pp.460-473, 1978.
14. Ishikawa, Y., R. Subramanya and C. Faloutsos, Mindreader: Querying databases through multiple examples, VLDB 1998, pp.218-227, 1998.
15. Tong, S. and E. Chang, Support vector machine active learning for image retrieval, Proc. of the 9th ACM International Conference on Multimedia, ACM Press, pp.107-118, 2001.
16. Flickner, M., H. Sawhney, W. Niblack, J. Ashley, Q. Huang and B. Dorn, Query by image and video content: The QBIC system, IEEE Computer vol.28, no.9, pp.23-32, 1995.
17. Kita, K., K. Tsuda and M. Shishibori, Information Retrieval Algorithms, Kyoritsu Publish, Tokyo, 2002.
18. Sclaroff, S., L. Taycher and M. L. Cascia, ImageRover: A content-based image browser for the World Wide Web, Proc. of the IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997.