论文标题
如何缩放快速置图像分割的超参数
How to scale hyperparameters for quickshift image segmentation
论文作者
论文摘要
QuickShift是一种流行的图像分割算法，在许多应用程序中用作预处理步骤。不幸的是，了解超级参数对该方法产生的超像素的数量和形状的影响非常具有挑战性。在本文中，我们从理论上研究了QuickShift算法的稍微修改版本，特别强调了I.I.D的均匀图像贴片。此类斑块之间的像素噪声和锋利的边界。利用此分析，我们得出了一种简单的启发式，以相对于图像大小缩放Quick Shift Hyperparameter，我们会通过经验检查。
Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameters' influence on the number and shape of superpixels produced by the method. In this paper, we study theoretically a slightly modified version of the quickshift algorithm, with a particular emphasis on homogeneous image patches with i.i.d. pixel noise and sharp boundaries between such patches. Leveraging this analysis, we derive a simple heuristic to scale quickshift hyperparameters with respect to the image size, which we check empirically.
