Relevance Feedback in Content-Based Image Retrieval: Some Recent Advances

Proceedings of the Joint Conference on Information Sciences

Volumn 6, Issue , 2002, Pages 15-18

  Zhou, Xiang Sean ^a   Huang, Thomas S ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONFORMAL MAPPING; DATABASE SYSTEMS; FEEDBACK; IMAGE PROCESSING; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; REAL TIME SYSTEMS; SEMANTICS; VECTORS;

BIASED DISCRIMINANT ANALYSIS (BDA); RELEVANCE FEEDBACK;

CONTENT BASED RETRIEVAL;

EID: 1642362412     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. G. Salton, Automatic text processing, Reading, Mass.: Addison-Wesley. 1989.
2. X.S. Zhou and T.S. Huang. "Exploring the nature and variants of relevance feedback," in proc. IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL), in conjunction with CVPR. Hawaii. Dec. 2001.
3. X.S. Zhou and T.S. Huang. "Small sample learning during multimedia retrieval using BiasMap," in proc. Int'l Conf. Computer Vision and Pattern Recognition. Hawaii. Dec. 2001.
4. K. Barnard and D.A. Forsyth. "Learning the semantics of words and pictures," in proc. Int'l Conf. on Computer Vision. Vancouver. July 2001.
5. X.S. Zhou and T.S. Huang. "Unifying keywords and contents for image retrieval," in proc. International Workshop on Content-Based Multimedia Indexing. Italy. Sept. 2001.
6. V. Vapnik, The nature of statistical learning theory, New York: Springer. 1995.
7. S. Minka, et al. "Fisher discriminant analysis with kernels," in proc. IEEE workshop on Neural Networks for Signal Processing 1999.
8. X.S. Zhou and T.S. Huang, Edge-based structural features for content-based image retrieval. Pattern Recognition Letters. 22.2001 p. 457-468.
9. Y. Rui and T.S. Huang. "Optimizing learning in image retrieval," in proc. IEEE Conf. Computer Vision and Pattern Recognition. South Carolina. June 2000.