  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.
