A distributed context-free grammars learning algorithm and its application in video classification

Jing Huang, D. Schonfeld
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引用次数: 2

Abstract

In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that the SCSGs model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential computing framework. A series of statistical algorithms are expected to learn SCFGs subsequently. The SGSCs can be then used to represent multiple-trajectory. Experimental results demonstrate the improved performance of our method compared with existing methods for multiple-trajectory classification.
分布式无上下文语法学习算法及其在视频分类中的应用
本文提出了一种新的随机上下文敏感语法(SCSGs)统计估计算法。首先,我们证明了SCSGs模型可以通过将其分解为几个因果随机上下文自由语法(scfg)模型来求解,并且每个scfg模型都可以使用完全同步的分布式计算框架同时求解。在实际序列计算框架的假设下,给出了一种基于多scfg近似解的替代更新方案。随后,期望通过一系列的统计算法来学习scfg。SGSCs可以用来表示多轨迹。实验结果表明,与现有的多弹道分类方法相比,该方法的性能有所提高。
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