Visual information retrieval using synthesized imagery

Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007

Volumn , Issue , 2007, Pages 127-130

  Thomee, Bart ^a   Huiskes, Mark J ^a   Bakker, Erwin ^a   Lew, Michael S ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

Author keywords

Artificial imagination; Content based retrieval; Principal component analysis

Indexed keywords

ARTIFICIAL IMAGINATION; INTERACTIVE SEARCH; RELEVANCE FEEDBACK; SYNTHETIC IMAGERY;

FEEDBACK; ITERATIVE METHODS; PRINCIPAL COMPONENT ANALYSIS; USER INTERFACES;

CONTENT BASED RETRIEVAL;

EID: 36849049696     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282280.1282303     Document Type: Conference Paper

References (21)

1. BAECK, T., FOGEL, D.B., AND MICHALEWICZ, Z. 1997. Handbook: of Evolutionary Computing, Oxford University Press, New York.
2. BEYER, H.-G. AND SCHWEFEL, H.-P. 2002. Evolution Strategies: A Comprehensive Introduction, Journal of Natural Computing 1(1), 3-52.
3. CHANG, S.-F., CHEN, W., AND SUNDARAM, H. 1998. Semantic Visual Templates: Linking Visual Features to Semantics. In: Proceedings of the IEEE International Conference on Image Processing, IEEE Computer Society Press, Los Alamos, Calif. 531-535.
4. CHEN, Y., ZHOU, X.S., AND HUANG, T.S. 2001. One-class SVM for Learning in Image Retrieval, In: Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, October, 815-818.
5. BACH, J.R., FULLER, C., GUPTA, A., HAMPAPUR, A., HOROWITZ, B., HUMPHREY, R., JAIN, R., AND SHU, C.-F. 1996. Virage Image Search Engine: An Open Framework for Image Management. In: Proceedings of the SPIE Storage and Retrieval for Still Image and Video Databases, California, USA, 76-87.
6. FLICKNER, M., SAWHNEY, H., NIBLACK, W., ASHLEY, J., QIAN HUANG, 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, September, 23-32.
7. GUO, G., ZHANG, H.-J., AND LI, S.Z. 2001. Boosting for Content-Based Audio Classification and Retrieval: An Evaluation, In: Proceedings of the IEEE Conference on Multimedia and Expo, Tokyo, Japan, August.
8. HAAS, M., RIJSDAM, J., THOMEE, B., AND LEW, M.S. 2004. Relevance Feedback: Perceptual Learning and Retrieval in Bio-computing, Photos, and Video, In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, New York, October, 151-156.
9. HE, X., MA, W.-Y., KING, O. LI, M., AND ZHANG, H. 2002. Learning and Inferring a Semantic Space from User's Relevance Feedback for Image Retrieval. In: Proceedings of the ACM. Multimedia. ACM, New York, 343-346.
10. HUISKES, M.J. 2005. Aspect-based Relevance Learning for Image Retrieval. In: Proceedings of the International Conference on Image and Video Retrieval, Singapore, Lecture Notes in Computer Science 3568, 639-649.
11. LEW, M.S., SEBE, N., DJERABA, C., AND JAIN, R. 2006. Content-based Multimedia Information Retrieval: State of the Art and Challenges. ACM. Transactions on Multimedia Computing, Communications, and Applications 2(1), 1-19.
12. MULLER, H., MULLER, W., MARCHAND-MAILLET, S., PUN, T., AND SQUIRE, D. 2000. Strategies for Positive and Negative Relevance Feedback in Image Retrieval. In: Proceedings of 15th International Conference on Pattern Recognition, Barcelona, Spain, September, 1043-1046.
13. ROCCHIO, 1971. Relevance Feedback in Information Retrieval. In: The Small Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed. Prentice Hall, Englewoods Cliffs.
14. ROWE, L.A. AND JAIN, R. 2005. ACM SIGMM Retreat Report on Future Directions in Multimedia Research. In: ACM Transactions on Multimedia Computing, Communications, and Application 1(1), 3-13.
15. SCLAROFF, S., LA CASCIA, M., SETHI, S., AND TAYCHER, L. 2001. Mix and Match Features in the ImageRover Search Engine. In: Principles of Visual Information Retrieval, M.S. LEW, Ed. Springer-Verlag, London, 259-277.
16. SHIR, O. AND BAECK, T. 2005. Niching in Evolution Strategies. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO'05, Washington D.C.
17. TIEU, K. AND VIOLA, P. 2004. Boosting Image Retrieval, International Journal of Computer Vision 56(1), 17-36.
18. THERRIEN, C. 1989. Decision, Estimation, and Classification, John Wiley and Sons, New York.
19. th ACM Internationa] Conference on Multimedia, 107-118.
20. YIN, P.-Y., BHANU, B., CHANG, K.-C, AND DONG, A. 2005. Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1536-1551.
21. ZHOU, X.S. AND HUANG, T.S. 2003. Relevance Feedback in Image Retrieval: A Comprehensive Review, Multimedia Systems Journal 8(6), Springer-Verlag, Heidelberg, 536-544.