MultiMedia Modeling: 23rd International Conference, MMM by Laurent Amsaleg, Gylfi Þór Guðmundsson, Cathal Gurrin, Björn

By Laurent Amsaleg, Gylfi Þór Guðmundsson, Cathal Gurrin, Björn Þór Jónsson, Shin'ichi Satoh

The two-volume set LNCS 10132 and 10133 constitutes the completely refereed lawsuits of the twenty third foreign convention on Multimedia Modeling, MMM 2017, held in Reykjavik, Iceland, in January 2017.

Of the 149 complete papers submitted, 36 have been chosen for oral presentation and 33 for poster presentation; of the 34 certain consultation papers submitted, 24 have been chosen for oral presentation and a pair of for poster presentation; furthermore, five demonstrations have been authorised from eight submissions, and all 7 submissions to VBS 2017. All papers awarded have been conscientiously reviewed and chosen from 198 submissions.

MMM is a number one foreign convention for researchers and practitioners for sharing new rules, unique examine effects and sensible improvement stories from all MMM comparable components, largely falling into 3 different types: multimedia content material research; multimedia sign processing and communications; and multimedia functions and services.

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Additional info for MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4-6, 2017, Proceedings, Part II

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There are several general issues when users try to solve the known-item search task. One group of users focuses too much on drawing a sketch and miss very promising results that were shown during the process (observed by the administrator). Another group looks only for a certain part of the video and when another part appears in the results, they miss it. Some users scroll 12 J. Lokoˇc et al. Fig. 6. The number of first page appearances of the searched scene with 3 s tolerance. (Color figure online) randomly through a promising video.

Surv. 40(2), 5:1–5:60 (2008) 7. : AAU video browser: non-sequential hierarchical video browsing without content analysis. , Breiteneder, C. ) MMM 2012. LNCS, vol. 7131, pp. 639–641. Springer, Heidelberg (2012). 1007/978-3-642-27355-1 63 8. : DeCAF: a deep convolutional activation feature for generic visual recognition. 1531 (2013) 9. : ImageNet classification with deep convolutional neural networks. , Weinberger, K. ) Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, Nevada, US, 3–6 December 2012, pp.

Syst. Video Technol. 26, 346–359 (2016) 8. 265 Video Codec (2015). http://www. org/ 9. : Common test conditions and software reference configurations. tw Abstract. 25 kbps) cached facial expressions (when the bandwidth is scarce). Our solution consists of optimized approaches to: (i) choose representative facial expressions from training video frames and (ii) match an incoming Webcam frame against the pre-transmitted facial expressions. To the best of our knowledge, such approach has never been studied in the literature.

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