Seed extraction using superpixel-based SLIC for interactive image segmentation

For complex images, interactive image segmentation can achieve better segmentation performance than other approaches. However, many classical interactive segmentation methods are sensitive to the locations and number of seed pixels. We introduce a seed extraction method to address this issue. First, we propose a robust seed measure to select and mark the seed pixels that cover the whole image. We use the center pixel of each superpixel as a seed. Then, each center pixel is labeled by our seed measure. Second, we propose a relabeling method for addressing isolated seeds. The extracted seeds can be used to locate the initial object area and can serve as the inputs of other interactive segmentation methods to reduce their dependence on user interaction. Extensive experiments on the Berkeley, GrabCut, and MSRA1000 datasets demonstrate the effectiveness of our seed extraction method. (C) 2022 SPIE and IS&T