An evaluation of the compactness of superpixels
An evaluation of the compactness of superpixels
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DOI：
10.1016/j.patrec.2013.09.013
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Alexander Schick;Mika Fischer;R. Stiefelhagen
中科院分区：
文献类型：
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作者：
Alexander Schick;Mika Fischer;R. Stiefelhagen
超像素分割是将图像过度分割成一组连通的同质区域。根据算法的不同，超像素具有特定的属性。几乎所有作者都声称他们的超像素具有紧凑性。然而，超像素的紧凑性还没有被测量，并且紧凑性的含义还没有被研究到超像素。作为我们的第一个贡献，我们提出了一个度量超像素紧凑性的度量。我们进一步讨论了紧凑性的含义，并通过一个实例应用演示了紧凑超像素的好处。最重要的是，我们发现紧凑性与边界回忆之间存在负相关。超像素分割的第二个所需特性是符合格子。这种规则结构类似于图像的像素网格，然后可以用于更有效的算法。作为我们的第二个贡献，我们提出了一个算法，它既提供了透明的、易于使用的紧凑性控制，又提供了可选的格子保证。我们用六种基准算法进行了评估，结果表明，该算法的性能优于最先进的算法。
Superpixel segmentation is the oversegmentation of an image into a connected set of homogeneous regions. Depending on the algorithm, superpixels have specific properties. One property that almost all authors claim for their superpixels is compactness. However, the compactness of superpixels has not yet been measured and the implications of compactness have not been investigated for superpixels. As our first contribution, we propose a metric to measure the compactness of superpixels. We further discuss implications of compactness and demonstrate the benefits of compact superpixels with an example application. Most importantly, we show that there is a negative correlation between compactness and boundary recall. A second desirable property for superpixel segmentations is conforming to a lattice. This regular structure, similar to the pixel grid of an image, can then be used for more efficient algorithms. As our second contribution, we propose an algorithm that offers both a transparent and easy-to-use compactness control with an optional lattice guarantee. We show in our evaluation with six benchmark algorithms, that the proposed algorithm outperforms the state-of-the-art.