Download Adaptive Multimedia Retrieval. Understanding Media and by Stephane Marchand-Maillet, Donn Morrison, Eniko Szekely, PDF

By Stephane Marchand-Maillet, Donn Morrison, Eniko Szekely, Jana Kludas, Marc Vonwyl (auth.), Marcin Detyniecki, Ana García-Serrano, Andreas Nürnberger (eds.)

This e-book constitutes the refereed court cases of the seventh overseas convention on Adaptive Multimedia Retrieval, AMR 2009, held in Madrid, Spain, in September 2009.The 12 revised complete papers and the invited contribution offered have been conscientiously reviewed. The papers are equipped in topical sections on greedy multimedia streams; pinpointing song; adapting distances; figuring out photos; and round the user.

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Additional info for Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User: 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009, Revised Selected Papers

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627–634 (2007) 9. : Binary tree based on-line video summarization. In: Proc. of ACM Multimedia 2008 (2nd ACM TRECVid Video Summarization Workshop), pp. 134– 138 (2008) 10. : On-line Video Skimming Based on Histogram similarity. In: Proc. of ACM Multimedia 2007 (1st ACM TRECVid Video Summarization Workshop), pp. 94–98 (2007) 11. : The TRECVID 2008 BBC rushes summarization evaluation. In: Proc. of ACM Multimedia 2008 (2nd ACM TRECVid Video Summarization Workshop), pp. 1–20 (2008) 12. : Video Shot Detection and Condensed Representation.

4. : CAIN-21: An extensible and metadata-driven multimedia adaptation engine in the MPEG-21 framework. , Bailer, W. ) SAMT 2009. LNCS, vol. 5887, pp. 114–125. Springer, Heidelberg (2009) 5. ISO/IEC 21000-2:2004, Information technology - Multimedia framework (MPEG-21) Part 2: Digital Item Declaration (2004) 6. ISO/IEC 15938-5:2003, Information technology - Multimedia content description interface - Part 5: Multimedia description schemes (2003) 7. net/ 8. : How many high-level concepts will fill the semantic gap in news video retrieval?

In the subsequent discussion, we employ normalized 12-dimensional chroma features with a temporal resolution of 2 Hz (2 features per second). Let V := (v 1 , v 2 , . . , vN ) and W := (w1 , w2 , . . , wM ) be two chroma feature sequences. To relate two chroma vectors we use the cosine distance defined by c(v n , wm ) = 1 − v n , wm for normalized vectors. By comparing the features of the two sequences in a pairwise fashion, one obtains an (N × M )-cost matrix C defined by C(n, m) := c(v n , wm ), see Fig.

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