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首页> 外文会议>2010 Second International Conference on Multimedia and Information Technology (MMIT 2010) >An Adaptive Ordering Framework for Filtering Multimedia Streams
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An Adaptive Ordering Framework for Filtering Multimedia Streams

机译：用于过滤多媒体流的自适应排序框架

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摘要

In multimedia stream filtering scenario, there usually exist many filtering rules that specify the filtering objectives and many filtering units that estimate the filtering rules. A filtering rule may connect to several different filtering units and a filtering unit may connect to several different filtering rules. An open problem in such a filtering scenario is how to order the filtering units in an optimal sequence so as to decrease the filtering cost. Existing methods are based on a greedy strategy which orders the filtering units according to three factors of the filtering units, i.e., the selectivity, popularity, and cost. Although all these methods reported good results, there is still one important problem that hasn't been addressed yet. The selectivity factor is set empirically, which is unable to adaptively adjust with stream passing by. Under these observations, in this paper, we propose an Adaptive ordering framework (AOF) which executes an adaptive ordering strategy. In AOF, all the temporal filtering results are preserved in each sliding window. Accordingly, the selectivity can adjust automatically and thus all the filtering units can be ordered with respect to the adapted selectivity. Experiments on both synthetic and real life multimedia streams demonstrate that our AOF method outperforms other simple filtering methods.
机译：在多媒体流过滤场景中，通常存在许多指定过滤目标的过滤规则和许多估计过滤规则的过滤单元。过滤规则可以连接到几个不同的过滤单元，并且过滤单元可以连接到几个不同的过滤规则。在这样的过滤场景中的开放问题是如何以最佳顺序对过滤单元进行排序以降低过滤成本。现有方法基于贪婪策略，该贪婪策略根据过滤单元的三个因素，即选择性，受欢迎度和成本来对过滤单元进行排序。尽管所有这些方法都报告了良好的结果，但是仍然存在一个尚未解决的重要问题。选择性系数是凭经验设置的，无法随流经过而自适应调整。在这些观察下，本文提出了一种自适应排序框架（AOF），该框架执行自适应排序策略。在AOF中，所有时间滤波结果都保留在每个滑动窗口中。因此，选择性可以自动调节，因此可以相对于适应的选择性对所有过滤单元进行排序。在合成和现实生活中的多媒体流上进行的实验表明，我们的AOF方法优于其他简单的过滤方法。

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