Abstract
Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel shape is mainly measured by its circularity. In this work, we first demonstrate that such measure is not adapted for superpixel evaluation, since it does not directly express regularity but circular appearance. Then, we propose a new metric that considers several shape regularity aspects: convexity, balanced repartition, and contour smoothness. Finally, we demonstrate that our measure is robust to scale and noise and enables to more relevantly compare superpixel methods.
Abstract (translated)
对于大多数基于超像素的物体识别或跟踪应用程序来说，常规的分解是必要的。到目前为止，在文献中，超像素形状的规则性或紧凑性主要是由它的圆度来测量的。在这项工作中，我们首先证明了这种方法不适用于超像素评估，因为它不直接表示规则性，而是圆形外观。然后，我们提出了一个新的度量，它考虑了形状规则性的几个方面：凸性、平衡重划分和轮廓平滑度。最后，我们证明了我们的测量方法对尺度和噪声具有鲁棒性，并且能够更相关地比较超像素方法。
URL
https://arxiv.org/abs/1903.07146
