Please use this identifier to cite or link to this item:
|Title:||Experiential sampling on multiple data streams|
|Authors:||Kankanhalli, M.S. |
|Source:||Kankanhalli, M.S., Wang, J., Jain, R. (2006). Experiential sampling on multiple data streams. IEEE Transactions on Multimedia 8 (5) : 947-955. ScholarBank@NUS Repository. https://doi.org/10.1109/TMM.2006.879875|
|Abstract:||Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many real-time applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it to build the model of the environment. We formulate this problem as an experiential sampling problem and propose an approach to utilize computing resources efficiently on the most informative subset of data streams. In this paper, we generalize our experiential sampling framework to multiple data streams and provide an evaluation measure for this technique. We have successfully applied this framework to the problems of traffic monitoring, face detection and monologue detection. © 2006 IEEE.|
|Source Title:||IEEE Transactions on Multimedia|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Jan 31, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.