References - Multimedia Database Retrieval: Technology and Applications - page 328

References

1. Bohn R., and Short. J.: How much information? 2009 Report on American Consumers.
University of California at San Diego, Global Information Industry Center , (2010)
2. http://www.youtube.com/t/press_statistics , (2012)
3. http://usatoday30.usatoday.com/tech/news/2010-07-21-facebook-hits-500-million-users_N.
htm , July (2014)
4. http://newsroom.fb.com/content/default.aspx?NewsAreaId=22 , (2011)
5. R. Yan and W. Hsu.: Content-based and concept-based analysis for large-scaleimage/video
retrieval. Proc. ACM MM, 913-914, (2009)
6. Y. Rui, T. Huang, and S. Chang.: Image retrieval: Current techniques, promising directions,
and open issues. J. of visual communication and image representation, 39-62, (1999)
7. R. Datta, D. Joshi, J. Li, and J. Wang.: Image retrieval: Ideas, influences, and trends of the
new age. ACM Computing Surveys (CSUR), (2008)
8. R. Baeza-Yates and B. Ribeiro-Neto.: Modern Information Retrieval, ACM Press, New York,
(1999)
9. Chen, T., Yap, K. H.: Discriminative BoW Framework for Mobile Landmark Recognition.
(2014)
10. Ji, R., Duan, L. Y., Chen, J., Yao, H., Yuan, J., Rui, Y., Gao, W.: Location discriminative
vocabulary coding for mobile landmark search. Int. J. of Computer Vision, 96 (3), 290-314,
(2012)
11. Rui, Y., Hang, T. S., Mehrotra, S., Ortega, M.: A relevance feedback architecture for content-
based multimedia information retrieval systems. Proc. IEEE Workshop on Content-based
Access of Image and Video Libraries, 82-89 (1997)
12. Rui, Y. Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in
MARS. Proc. IEEE Int. Conf. on Image Processing, Washington D.C., USA. 815-818 (1997)
13. Salton, G., McGill, M. J.: Introduction to Modern Information Retrieval. McGraw-Hill Book
Company, NY. (1983)
14. Celentano, A., Sciasicio, E. D.: Feature interaction and relevance feedback analysis in image
similarity evaluation. J. of Electronic Imaging, 7 308-317 (1998)
15. Müller, H., Müller, W., Marchand-Maillet, S., Pun, T., Squire, D. M.: Strategies for positive
and negative relevance feedback in image retrieval. Int. Conf. on Pattern Recognition,
Barcelona, Spain. 1 1043-1046 (2000)
16. Peng, J., Bhanu, B., Qing, S.: Probabilistic feature relevance learning for content-based image
retrieval. Computer Vision and Image Understanding. 75 150-164 (1999)