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Experiential sampling on multiple data streams

Kankanhalli, MS; Wang, J; Jain, R; (2006) Experiential sampling on multiple data streams. IEEE Transactions on Multimedia , 8 (5) pp. 947-955. 10.1109/TMM.2006.879875. Green open access

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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.

Type: Article
Title: Experiential sampling on multiple data streams
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMM.2006.879875
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/12213
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