By Shih-Fu Chang (auth.), Peter Enser, Yiannis Kompatsiaris, Noel E. O’Connor, Alan F. Smeaton, Arnold W. M. Smeulders (eds.)
This booklet constitutes the refereed complaints of the 3rd foreign convention on snapshot and Video Retrieval, CIVR 2004, held in Dublin, eire in July 2004.
The 31 revised complete papers and forty four poster papers offered have been conscientiously reviewed and chosen from one hundred twenty five submissions. The papers are prepared in topical sections on snapshot annotation and person looking out, picture and video retrieval algorithms, individual and occasion identity for retrieval, content-based picture and video retrieval, and consumer perspectives.
Read or Download Image and Video Retrieval: Third International Conference, CIVR 2004, Dublin, Ireland, July 21-23, 2004. Proceedings PDF
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Extra info for Image and Video Retrieval: Third International Conference, CIVR 2004, Dublin, Ireland, July 21-23, 2004. Proceedings
Sample text
Training such models boils down to the estimation of the parameters of the class posterior probability p(y|x) which is assumed to have a known form. While the discriminative probabilistic models have similar performance as SVM, they need only unconstrained optimization, and furthermore, are suitable for probabilistic analysis. The later advantages make the models more flexible in meeting sophisticated criteria in data selection. The section presents two new active learning methods for the discriminative probabilistic models.
For the one active fragment, a lot of scent carriers are available: keyframes (in this case, nine), transcript (derived from closed captions [when available] and/or speech recognition), displayed text in the video (optical character recognition), and added description. Of course, the video images– that can be either viewed or browsed by fast-forwarding or using a slider - can also carry scent by themselves. Regarding switching to other patches, the way indications about overlapping patches are presented will influence the scent people perceive.
In Human Language Technology Conference Workshop on Learning Word Meaning from Non-Linguistic Data, 2003. 7. P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Seventh European Conf. on Computer Vision, pages 97-112, 2002. 8. R. Fergus, P. Perona and A Zisserman Object Class Recognition by Unsupervised Scale-Invariant Learning. IN Proc. CVPR’03, vol II pages 264-271, 2003. 9. J. Jeon, V. Lavrenko and R.