By James R. Wilcox
This targeted factor contains the various remarkable paintings initially provided on the ACM Multimedia convention 2003 (ACM MM 2003). The convention obtained 255 submissions, of which forty three top of the range papers have been authorised for presentation. of those papers, the Technical software Chairs invited a dozen authors to publish more suitable types in their papers to this distinct factor. those papers went via a rigorous overview strategy, and we're satisfied to give 4 really notable papers during this specialissue. because of the hugely aggressive evaluate method and restricted area, many fantastic papers couldn't be authorized for this distinctive factor. despite the fact that, a few of them are being forwarded for attention as destiny general papers during this magazine.
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Additional info for ACM Transactions on Multimedia Computing, Communications and Applications (May)
2002; Wenyin et al. 2001; Wu et al. 2002]. All these approaches assume that C, P , and L are fixed. The ability to improve C, P , and L is where our proposed CDE approach differs from these static ones. The core of CDE is its ability to assess classification confidence. The concept of classification confidence is not new in the field of pattern recognition. Chow  proposed using the “rejection” option to refuse classifying a low-confidence instance. To measure confidence, various methods [Bouchaffra et al.
For the bags with higher confidence, their votes should be given greater consideration during the final tally. Thus, we weigh each bag’s vote by the confidence factor CFm (ωb). The final prediction is formulated as = argmax1≤c≤M CFm (ωb). (7) ωb =c To evaluate the confidence level of the overall prediction, we identify two useful parameters: Definition 3. Top Voting Score Vp = CFm (ωb). ωb = Definition 4. Voting Margin Vm = V p − max 1≤c≤M ,c= CFm (ωb), ωb =c under the situation of unanimous voting, Vm = V p .
Note that the buffering and streaming algorithm can accept any number of priority layers and arbitrary application-specific mappings from video data to priority levels. 5 Providing Adaptive Sensor Functionality. In our initial implementation of the sensor, we simply modularized the code and connected the code via function calls. In order to change one of the parts such as the compression algorithm or the way filtering is accomplished, requires all of the code to be recompiled and sent to the sensor.