Ma Dongyang, Zhou Yuanfeng, Xin Shiqing, Wang Wenping
IEEE Trans Image Process. 2021;30:1825-1839. doi: 10.1109/TIP.2020.3045640. Epub 2021 Jan 18.
Superpixel segmentation, as a central image processing task, has many applications in computer vision and computer graphics. Boundary alignment and shape compactness are leading indicators to evaluate a superpixel segmentation algorithm. Furthermore, convexity can make superpixels reflect more geometric structures in images and provide a more concise over-segmentation result. In this paper, we consider generating convex and compact superpixels while satisfying the constraints of adhering to the boundary as far as possible. We formulate the new superpixel segmentation into an edge-constrained centroidal power diagram (ECCPD) optimization problem. In the implementation, we optimize the superpixel configurations by repeatedly performing two alternative operations, which include site location updating and weight updating through a weight function defined by image features. Compared with existing superpixel methods, our method can partition an image into fully convex and compact superpixels with better boundary adherence. Extensive experimental results show that our approach outperforms existing superpixel segmentation methods in boundary alignment and compactness for generating convex superpixels.
超像素分割作为一项核心图像处理任务，在计算机视觉和计算机图形学中有许多应用。边界对齐和形状紧凑性是评估超像素分割算法的主要指标。此外，凸性可使超像素反映图像中更多的几何结构，并提供更简洁的过分割结果。在本文中，我们考虑在尽可能遵循边界约束的情况下生成凸且紧凑的超像素。我们将新的超像素分割问题表述为一个边缘约束质心幂图（ECCPD）优化问题。在实现过程中，我们通过反复执行两种交替操作来优化超像素配置，这两种操作包括通过由图像特征定义的权重函数进行位点位置更新和权重更新。与现有超像素方法相比，我们的方法能够将图像分割为具有更好边界遵循性的完全凸且紧凑的超像素。大量实验结果表明，在生成凸超像素的边界对齐和紧凑性方面，我们的方法优于现有超像素分割方法。
