Bayesian Network for Algorithm Selection: Real-World Hierarchy for Nodes Reduction
Bayesian Network for Algorithm Selection: Real-World Hierarchy for Nodes Reduction
复制标题
用于算法选择的贝叶斯网络：节点减少的真实世界层次结构
DOI：
10.1109/icawst.2013.6765411
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Lukac M. and Kameyama M.
中科院分区：
文献类型：
--
作者：
Katsuhiro Ueno;Atsushi Ohori;Lukac M. and Kameyama M.
为了在图像理解中获得最佳结果，希望在逐个情况的基础上选择最佳算法。可以仅使用图像特征来选择算法，然而，由于遮挡、阴影和其他环境条件，这样选择的算法通常会产生错误。为了避免这种错误，有必要在符号级别上理解处理错误。然而，使用符号信息来确定最佳算法是困难的任务，因为元素和环境条件的可能组合几乎是无限的。因此，不可能为对象、环境条件和上下文变化的所有可能组合预测最佳算法。本文研究了基于符号图像描述的算法选择和基于高层图像描述的算法误差判定。所提出的方法变换和最小化的符号图像描述中包含的高层次的信息，以这种方式，将保持算法选择的质量。该转换采用一个高层次的信息标签，并将其转换为一组通用的功能，而最小化使用层次结构，以减少信息的具体性质。这两种信息约简方法都用于贝叶斯网络，因为BN以使用泛化和层次而闻名。如本文所示，这种表示有效地减少了细粒度的高层次的符号描述，以粗粒度的层次结构，保持选择质量，但减少了节点的数量。
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.