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By Samy Bengio, Herve Bourlard

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Additional info for Machine Learning for Multimodal Interaction: First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers ... Applications, incl. Internet Web, and HCI)

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Rotating the mug in response to head movements in the video results in a real-time annotation of the head orientation of the person in the video. A small 3D display of a virtual head follows the mug for verification of the annotation quality. For fragments with fast head movements the video speed can be slowed down. On those fragments an efficiency ratio of 1:2 per annotated person can be achieved. Fig. 3. 3 Semi-Automatic Annotation Apart from interface improvements in order to increase the annotation efficiency, tools are developed to perform annotations automatically.

Kluwer, Dordrecht (NL) (2003) 31. : Video collaborative annotation forum: Establishing ground-truth labels on large multimedia datasets. In: Proc of the TRECVID2003. (2003) 32. : Hand and Mind: What gestures reveal about thought. University of Chicago Press (1995) 33. : Face tracking and hand gesture recognition for human-robot interaction. In: Proc. of the International Conference on Robotics and Automation. (2004) 34. edu/annotation/. 35. : The NITE XML toolkit: flexible annotation for multi-modal language data.

This will obviously increase as time goes on and everyone begins to have access to digitised photos and videos throughout their lives. One can see this as an attempt to establish the narrative of one’s life: what drives the most literate segment of the population to write autobiographies (for the children) even when, objectively speaking, they may have lived lives with little to report. But think of what will be needed if a huge volume of material is to be sorted. A related notion of “memories for life” has been developed and recently been declared a major challenge for future UK computing and we shall discuss this whole matter again later.

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