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首页> 外文期刊>Electronic Letters on Computer Vision and Image Analysis: ELCVIA >Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing
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Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing

机译：纹理图像处理中非循环邻接图组合参数的选择算法

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Nowadays the great interest of researchers in the problem of processing the interrelated data arrays including images is retained. In the modern theory of machine learning, the problem of image processing is often viewed as a problem in the field of graph models. Image pixels constitute a unique array of interrelated elements. The interrelations between array elements are represented by an adjacency graph. The problem of image processing is often solved by minimizing Gibbs energy associated with corresponding adjacency graphs. The crucial disadvantage of Gibbs approach is that it requires empirical specifying of appropriate energy functions on cliques.?In the present work, we investigate a simpler, but not less effective model, which is an expansion of the Markov chain theory.?Our approach to image processing is based on the idea of replacing the arbitrary adjacency graphs by tree-like (acyclic in general) ones and linearly combining of acyclic Markov models in order to get the best quality of restoration of hidden classes. In this work, we propose algorithms for tuning combination of acyclic adjacency graphs.
机译：如今，研究人员对处理包括图像在内的相互关联的数据阵列的问题保持了极大的兴趣。在现代机器学习理论中，图像处理问题通常被视为图模型领域的问题。图像像素构成相关元素的唯一阵列。数组元素之间的相互关系由邻接图表示。通常通过最小化与相应邻接图相关的吉布斯能量来解决图像处理问题。吉布斯方法的关键缺点是，它需要根据经验在群上指定适当的能量函数。在当前工作中，我们研究了一种更简单但有效程度不高的模型，该模型是马尔可夫链理论的扩展。图像处理的基本思想是用树状（通常是非循环的）树替换任意邻接图，并线性组合非循环的马尔可夫模型，以获得最佳的隐藏类恢复质量。在这项工作中，我们提出了用于调整非循环邻接图组合的算法。

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