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首页> 外文会议>2013 International Joint Conference on Awareness Science and Technology and Ubi-Media Computing >Bayesian Network for algorithm selection: Real-world hierarchy for nodes reduction
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Bayesian Network for algorithm selection: Real-world hierarchy for nodes reduction

机译：贝叶斯网络用于算法选择：减少节点的真实世界层次

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摘要

In order to obtain the best result in image understanding it is desirable to select the best algorithm on a case by case basis. An algorithm can be selected using only image features, however such selected algorithms will often generate errors due to occlusion, shadows and other environmental conditions. To avoid such errors, it is necessary to understand processing errors on a symbolic level. Using symbolic information to determine the best algorithm is however difficult task because the possible combinations of elements and environmental conditions are almost infinite. Consequently it is impossible to predict best algorithm for all possible combinations of objects, environment conditions and context variations. In this paper we investigate selection of algorithms using symbolic image description and the determination of algorithms' error from high level image description. The proposed method transforms and minimize the high level information contained in the symbolic image description in such manner that will preserve the algorithm selection quality. The transformation takes a high level information label and transforms it into a set of generic features while the minimization uses hierarchy to reduce the specific nature of the information. Both methods of information reduction are used in a Bayesian Network because a BN is well known for using the generalization and hierarchy. As is shown in this paper, such representation efficiently reduces the fine grain high-level symbolic description to a coarser-grain hierarchy that preserves the selection quality but reduces the number of nodes.
机译：为了在图像理解中获得最佳结果，希望根据具体情况选择最佳算法。只能使用图像特征来选择算法，但是由于遮挡，阴影和其他环境条件，这种选择的算法通常会产生错误。为了避免此类错误，有必要在符号级别上了解处理错误。但是，使用符号信息来确定最佳算法是一项艰巨的任务，因为元素和环境条件的可能组合几乎是无限的。因此，不可能针对对象，环境条件和上下文变化的所有可能组合预测最佳算法。在本文中，我们研究了使用符号图像描述的算法选择以及根据高级图像描述确定算法错误的方法。所提出的方法以将保留算法选择质量的方式来变换和最小化包含在符号图像描述中的高级信息。转换采用高级信息标签，并将其转换为一组通用功能，而最小化则使用层次结构来减少信息的特定性质。贝叶斯网络中使用了两种信息约简方法，因为众所周知，BN因使用泛化和层次结构而闻名。如本文所示，这种表示有效地将细粒度高级符号描述减少为粗粒度层次结构，从而保留了选择质量，但减少了节点数量。

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