Superpixel Segmentation by Forgetting Geodesic Distance

Abstract

Superpixel segmentation could be of benefit to computer vision tasks due to its perceptually meaningful results with similar appearance and location. To obtain the accurate superpixel segmentation, existing methods introduce geodesic distance to fit the object boundaries. However, conventional geodesic distance easily suffers from error accumulation and excessive time consumption. This paper proposes a fast superpixel segmentation method based on a new geodesic distance, called forgetting geodesic distance. In contrast to the conventional geodesic distance, the forgetting geodesic distance utilizes a forgetting factor to gradually reduce the influence of previous path cost and focuses on the latest pixels' difference. Intuitively, a pixel with lower difference with respect to the latest path contextual distance will be more similar with the corresponding region. In the new path, the path cost devotes much greater attention to the latest pixels' difference and could significantly relieve error accumulation. The pixels are also aggregated with less dependence on seeds as the path extends, which avoids the seed updating. The experimental results validate that the proposed method obtains 2 percent and 1 percent gain on average compared with most of the state-of-the-art methods in terms of BSD500 and VOC2012 datasets, respectively.