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|Title:||Goal-oriented optimal subset selection of correlated multimedia streams|
Optimal media selection
|Citation:||Atrey, P.K., Kankanhalli, M.S., Oommen, J.B. (2007). Goal-oriented optimal subset selection of correlated multimedia streams. ACM Transactions on Multimedia Computing, Communications and Applications 3 (1). ScholarBank@NUS Repository. https://doi.org/10.1145/1198302.1198304|
|Abstract:||A multimedia analysis system utilizes a set of correlated media streams, each of which, we assume, has a confidence level and a cost associated with it, and each of which partially helps in achieving the system goal. However, the fact that at any instant, not all of the media streams contribute towards a system goal brings up the issue of finding the best subset from the available set of media streams. For example, a subset of two video cameras and two microphones could be better than any other subset of sensors at some time instance to achieve a surveillance goal (e.g. event detection). This article presents a novel framework that finds the optimal subset of media streams so as to achieve the system goal under specified constraints. The proposed framework uses a dynamic programming approach to find the optimal subset of media streams based on three different criteria: first, by maximizing the probability of achieving the goal under the specified cost and confidence; second, by maximizing the confidence in the achieved goal under the specified cost and probability with which the goal is achieved; and third, by minimizing the cost to achieve the goal with a specified probability and confidence. Each of these problems is proven to be NP-Complete. From an AI point of view, the solution we propose is heuristic-based, and for each criterion, utilizes a heuristic function which for a given problem, combines optimal solutions of small-sized subproblems to yield a potential near-optimal solution to the original problem. The proposed framework allows for a tradeoff among the aforementioned three criteria, and offers the flexibility to compare whether any one set of media streams of low cost would be better than any other set of higher cost, or whether any one set of media streams of high confidence would be better than any other set of low confidence. To show the utility of our framework, we provide the experimental results for event detection in a surveillance scenario. © 2007 ACM.|
|Source Title:||ACM Transactions on Multimedia Computing, Communications and Applications|
|Appears in Collections:||Staff Publications|
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