We propose a novel post-processing framework to improve foreground segmentations with the use of Probabilistic Superpixel Markov Random Fields. First, we convert a given pixel-based segmentation into a probabilistic superpixel representation. Based on these probabilistic superpixels, a Markov random field exploits structural information and similarities to improve the segmentation. We evaluate our approach on all categories of the Change Detection 2012 dataset. Our approach improves all performance measures simultaneously for eight different basis foreground segmentation algorithms.
Schick et al. REF convert the pixel-based segmentation into a probabilistic superpixel representation.
Improving foreground segmentations with probabilistic superpixel Markov random fields
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Computer Science