By James R. Wilcox
This particular factor contains a few of the impressive paintings initially provided on the ACM Multimedia convention 2003 (ACM MM 2003). The convention acquired 255 submissions, of which forty three top quality papers have been permitted for presentation. of those papers, the Technical application Chairs invited a dozen authors to post more advantageous models in their papers to this targeted factor. those papers went via a rigorous assessment strategy, and we're satisfied to provide 4 actually amazing papers during this specialissue. as a result of the hugely aggressive overview method and constrained house, many fantastic papers couldn't be authorized for this targeted factor. even though, a few of them are being forwarded for attention as destiny usual papers during this magazine.
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Extra resources for ACM Transactions on Multimedia Computing, Communications and Applications (May)
Using Margin as Part of CF. (a) Scatterplot of misclassified and correctly classified images, and (b) classification accuracy vs. multi-class margin with sigmoid fit. Parameters A, B, and C can be determined through empirical fitting as shown in Figure 1(b). With the margin tm , we formulate the confidence level of an annotation at multi-class level through the following function: CFm = t p × g (tm ). (6) CFm includes the consideration of multiple factors in determining the confidence of a prediction.
The results of the experiments for the Bitsy board are shown in Figure 8. From the beginning of the trace until about 6 seconds into the trace, the video sensor is turning on and configuring itself. During this time, the power being drawn by the sensor is highly variable as it configures and tests the various hardware components on the board. 5 watts). Seconds 10–13 show the video camera turned on without capturing. 5 watts of power to operate. Seconds 13–16 show the camera sleeping. Thus, over a watt of power can be saved if the sensor is incorporated with other low-power video sensor technologies that notify it when to turn on.
Upon receiving the query, the query manager finds all events within the system that have one of the regions in its event overview map. The list of matching events is then returned to the user. As an example, we have shown a sample query, in which the user highlighted part of the computer at the bottom of the image (see Figure 7). The query manager responded with only three events. Compared with the large list of events from the same camera in Figure 5, the simple bitmapping algorithm has reduced the number of events considerably.