Abstract
Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization. Many superpixel methods only rely on colors features for segmentation, limiting performance in low-contrast regions and applicability to infrared or medical images where object boundaries have wide appearance variability. We study the inclusion of deep image features in the SLIC superpixel algorithm to exploit higher-level image representations. In addition, we devise a trainable superpixel algorithm, yielding an intermediate domain-specific image representation that can be applied to different tasks. A clustering-based superpixel algorithm is transformed into a pixel-wise classification task and superpixel training data is derived from semantic segmentation datasets. Our results demonstrate that this approach is able to improve superpixel quality consistently.
Abstract (translated)
超像素算法是计算机视觉算法中常见的分割、目标跟踪和定位等预处理步骤。许多超像素方法仅依靠颜色特征进行分割,限制了低对比度区域的性能,并适用于物体边界具有广泛外观变异性的红外或医学图像。我们研究了在SLIC超像素算法中包含深度图像特征,以利用更高层次的图像表示。此外,我们还设计了一个可训练的超像素算法,产生一个中间域特定的图像表示,可以应用于不同的任务。将一种基于聚类的超像素算法转化为一种基于像素的分类任务,并从语义分割数据中提取超像素训练数据。实验结果表明,该方法能够持续提高超像素质量。
URL
https://arxiv.org/abs/1903.04586