ACM Transactions on Multimedia Computing, Communications and by James R. Wilcox

By James R. Wilcox

This particular factor contains a number of the notable paintings initially awarded on the ACM Multimedia convention 2003 (ACM MM 2003). The convention acquired 255 submissions, of which forty three fine quality papers have been approved for presentation. of those papers, the Technical application Chairs invited a dozen authors to publish stronger models in their papers to this precise factor. those papers went via a rigorous overview method, and we're chuffed to provide 4 really striking papers during this specialissue. as a result of hugely aggressive overview approach and restricted area, many fantastic papers couldn't be authorized for this specific factor. in spite of the fact that, a few of them are being forwarded for attention as destiny average papers during this magazine.

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3) Bag Level. To reduce classification variance, we employ the bagging scheme [Breiman 1996], which combines multiple bags of multi-class classifiers to make an overall prediction. An overall confidence factor is also estimated at this level. For a low overall confidence prediction, a new hierarchy of classifiers is dynamically constructed to improve prediction accuracy. ) If the overall confidence of the new ensemble is still low, we flag the instance as a potential candidate for new-information discovery.

This would allow the sensor network’s power consumption to be minimized. In addition to hardware sensors, there are a large number of sensor networking technologies that sit on top of the sensors themselves. These include technologies for ad hoc routing, location discovery, resource discovery, and naming. Clearly, advances in these areas can be incorporated into our video sensor technology. 3 Mobile Power Management Mobile power management is another key problem for long-lived video sensors. There have been many techniques focused on overall system power management.

Voting Margin Vm = V p − max 1≤c≤M ,c= CFm (ωb), ωb =c under the situation of unanimous voting, Vm = V p . ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 1, No. 2, May 2005. 174 • K. Goh et al. Finally, we define the confidence of the overall prediction as V p × g (Vm ) . (8) B The denominator in the equation above is to normalize the confidence factor to be within the range [0, 1]. A prediction with a high CF is more likely to be accurate. The formal description of the multilevel annotation algorithm is presented in Figure 2.

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