Image and Video Retrieval: Third International Conference, by Shih-Fu Chang (auth.), Peter Enser, Yiannis Kompatsiaris,

By Shih-Fu Chang (auth.), Peter Enser, Yiannis Kompatsiaris, Noel E. O’Connor, Alan F. Smeaton, Arnold W. M. Smeulders (eds.)

This publication constitutes the refereed lawsuits of the 3rd overseas convention on photograph 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 rigorously reviewed and chosen from one hundred twenty five submissions. The papers are equipped in topical sections on snapshot annotation and person looking, photograph and video retrieval algorithms, individual and occasion id for retrieval, content-based photo and video retrieval, and consumer perspectives.

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Extra info for Image and Video Retrieval: Third International Conference, CIVR 2004, Dublin, Ireland, July 21-23, 2004. Proceedings

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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. Manmatha. (2003) Automatic Image Annotation and Retrieval using Cross-Media Relevance Models In Proceedings of the 26th Intl. , pages 119–126, 2003 10. V. Lavrenko, M. Choquette, and W. Croft. Cross-lingual relevance models. Proceedings of the 25th Intl.

P. Brown, S. D. Pietra, V. D. Pietra, and R. Mercer. The mathematics of statistical machine translation: Parameter estimation. In Computational Linguistics, 19(2):263-311, 1993. 6. P. Carbonetto, N. de Freitas. Why can’t Jos´e read? The problem of learning semantic associations in a robot environment. 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.

When people perceive no scent, they should be able to perform a random walk in order to spot a trace of scent. When people perceive a lot of scent, they should be able to follow the trail to the target. When the scent gets low, people should be able to switch to other patches. These types of browsing behaviours all need to be supported in the design. Typical designrelated situations can be that browsing cues are misleading (the scent is high, but the target is not relevant/interesting) or badly presented (no or low scent, but a very relevant or interesting target).

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