Integration of the saliency-based seeds generation and random walks with restart for image segmentation

Abstract

In the task of interactive image segmentation, the user initially indicates some pixels as target seeds and background seeds and propagates the seeds’ labels to the rest of the image. We propose an equidistant seed generation method for interactive image segmentation that combines saliency detection and random walk (RW) with restart with optimal restarting probability. Our method includes two stages: first, we utilize the saliency detection method to obtain the initial target region and generate the initial seeds. Then we relabel the seeds using a Gaussian mixture model with limited user interaction to obtain accurate seeds. The relabeled seeds can limit the range of object; therefore, in the second stage, we can select an optimal restarting probability by calculating the area of the RW that exceeds the limited range of the object seeds. Our method can generate effective seeds and thus reduce the dependence on user interaction. Furthermore, using the optimal restarting probability, we can obtain higher segmentation accuracy. Extensive experiments on the Berkeley segmentation dataset, GrabCut dataset, and MSRA1000 dataset demonstrate that our method can reduce the dependence on user interaction and achieve better performance than the latest interactive image segmentation methods, such as RW, RW with restart, normalized RW, sub-Markov RW, and label propagation through complex networks.