Global-local optimizations by hierarchical cuts and climbing energies
Global-local optimizations by hierarchical cuts and climbing energies
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DOI：
10.1016/j.patcog.2013.05.012
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
B. R. Kiran;J. Serra
中科院分区：
文献类型：
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作者：
B. R. Kiran;J. Serra
分层分割是对图像进行多尺度分析，并提供一系列简化的嵌套分割。这种层次结构本身很少是目的，而是需要外部标准或启发式来解决图像分割、纹理提取和语义图像标记等问题。在这篇理论论文中，我们引入了一个新的框架：层次切割，来制定分段层次上的优化问题。其次，我们给出了解决分区选择的全局组合优化问题所必需的h-递增、奇异和尺度递增的三个重要概念，这些概念会导致线性时间动态规划。总结了此类能量的常见族，并描述了一种生成新族的方法。最后演示了该框架在图像分割和纹理增强问题上的应用。
Hierarchical segmentation is a multi-scale analysis of an image and provides a series of simplifying nested partitions. Such a hierarchy is rarely an end by itself and requires external criteria or heuristics to solve problems of image segmentation, texture extraction and semantic image labelling. In this theoretical paper we introduce a novel framework: hierarchical cuts, to formulate optimization problems on hierarchies of segmentations. Second we provide the three important notions ofh-increasing,singular, andscale increasingenergies, necessary to solve the global combinatorial optimization problem of partition selection and which results in linear time dynamic programs. Common families of such energies are summarized, and also a method to generate new ones is described. Finally we demonstrate the application of this framework on problems of image segmentation and texture enhancement.