Abstract We investigate the intersection between hierarchical and superpixel image segmentation. Two strategies are considered: (i) the classical region merging, that creates a dense hierarchy with a higher number of levels, and (ii) the recursive execution of some superpixel algorithm, which generates a sparse hierarchy with fewer levels. We show that, while dense methods can capture more intermediate or higher-level object information, sparse methods are considerably faster and usually with higher boundary adherence at finer levels. We first formalize the two strategies and present a sparse method, which is faster than its superpixel algorithm and with similar boundary adherence. We then propose a new dense method to be used as post-processing from the intermediate level, as obtained by our sparse method, upwards. This combination results in a unique strategy and the most effective hierarchical segmentation method among the compared state-of-the-art approaches, with efficiency comparable to the fastest superpixel algorithms.

中文翻译：

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使用超像素的密集和稀疏层次结构进行图像分割

摘要 我们研究了分层和超像素图像分割之间的交集。考虑了两种策略：(i) 经典区域合并，创建具有更多级别的密集层次结构，以及 (ii) 递归执行某些超像素算法，生成具有较少级别的稀疏层次结构。我们表明，虽然密集方法可以捕获更多的中间或更高级别的对象信息，但稀疏方法要快得多，并且通常在更精细的级别上具有更高的边界依从性。我们首先将这两种策略形式化并提出一种稀疏方法，该方法比其超像素算法更快，并且具有相似的边界粘附性。然后，我们提出了一种新的密集方法，用作中间级别的后处理，如通过我们的稀疏方法获得的，向上。