Relevance feedback based on incremental learning for mammogram retrieval

IEEE International Conference on Image Processing

Volumn 1, Issue , 2003, Pages 729-732

  El Naqa, Issam ^b   Yang, Yongyi ^a   Galatsanos, Nikolas P ^a   Wernick, Miles N ^a  

^a Illinois Institute of Technology   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FEEDBACK; FUNCTIONS; MAMMOGRAPHY; MATRIX ALGEBRA; MEDICAL IMAGING; PATTERN RECOGNITION; PERTURBATION TECHNIQUES; REGRESSION ANALYSIS;

RELEVANCE FEEDBACK (RF); SUPPORT VECTOR MACHINES (SVM);

CONTENT BASED RETRIEVAL;

EID: 0344235289     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. A. Bimbo, Visual Information Retrieval, Morgan Kauffman Publishers, 1999.
2. J. Peng et al, "Probabilistic feature relevance learning for content-based image retrieval," Comp. Vis. Imag. Under., 1999.
3. I. Cox et al, "The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments," IEEE Trans. on Image Processing, 2000.
4. S. Wong, "CBIR in medicine: still a long way to go," IEEE Workshop on Content-based Access of Image & Video Lib., 1998.
5. I. El Naqa, Y. Yang, N. Galatsanos, M. Wernick, "Content based image retrieval for digital mammography," ICIP, 2002.
6. V. Vapnik, Statistical Learning Theory, NY: Wiley, 1998.
7. N. Syed et al, "Incremental learning with support vector machines," Inter. Joint Conf. on Art. Intell., 1999.
8. G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," Adv. Neural Info. Proc. Sys., 2001.
9. rd Ed., 1998.