Multilabel Random Walker Image Segmentation Using Prior Models

The recently introduced random walker segmentation algorithm of [14] has been shown to have desirable theoretical properties and to perform well on a wide variety of images in practice. However, this algorithm requires user-specified labels and produces a segmentation where each segment is connected to a labeled pixel. We show that incorporation of a nonparametric probability density model allows for an extended random walkers algorithm that can locate disconnected objects and does not require user-specified labels. Finally, we show that this formulation leads to a deep connection with the popular graph cuts method of [8, 24].

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Leo Grady
• Keywords: Computer Vision, CVPR 2005, Extended Random Walkers, Nonparametric Probability Density, User-specified Labels, Walker Segmentation Algorithm