Abstract
Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art superpixel decomposition methods, a trade-off is made between 1) computational time, 2) adherence to image contours and 3) regularity and compactness of the decomposition. In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework. The distance computed when trying to associate a pixel to a superpixel during the clustering is enhanced by considering the linear path to the superpixel barycenter. The proposed framework produces regular and compact superpixels that adhere to the image contours. We provide a detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The obtained results outperform state-of-the-art methods in terms of standard superpixel and contour detection metrics.
Abstract (translated)
超像素分解方法通常作为一个预处理步骤来加速图像处理任务。他们将图像的像素分组成均匀的区域，同时试图尊重现有的轮廓。对于所有最先进的超级像素分解方法，在1）计算时间、2）图像轮廓的依附性和3）分解的规则性和紧凑性之间进行权衡。本文提出了一种在迭代聚类框架中利用线性路径（头皮）快速计算具有轮廓附着的超像素的方法。通过考虑到超像素重心的线性路径，在聚类期间尝试将像素与超像素关联时计算出的距离得到了增强。该框架产生了规则的、紧凑的、与图像轮廓相吻合的超像素。我们在标准伯克利分割数据集上对头皮进行了详细的评估。在标准超级像素和轮廓检测指标方面，获得的结果优于最先进的方法。
URL
https://arxiv.org/abs/1903.07149
