Interactive Image Segmentation using Optimization with Statistical Priors
Jian Guan, Guoping Qiu · 2006
Abstract. Interactive image segmentation is important and has widespread applications in computer vision, computer graphics and medical imaging. A recent work has shown that interactive figure ground segmentation can be achieved by computing a transparency image using an optimization framework, where user interactions are used to supply constraints for solving a quadratic cost function with a unique global minimum, which can be efficiently obtained using standard methods. In this paper, we introduce statistical priors as constraints to solve the optimization problem. We show that for some images, the statistical priors can provide good enough constraints to automatically obtain satisfactory figure ground segmentation results. For more difficult cases, we use the segmentation result of the statistical priors as a starting point for interactive figure ground segmentation. We show that segmentation results obtained based on statistical priors can be effectively employed to guide user interaction thus helping to reduce users labor in the interaction process. We also present a new effective adaptive thresholding method for making binary (hard) segmentation based on the computed continuous transparency image. Another contribution of this paper is the extension of the optimization based interactive figure ground segmentation framework to interactive multi-class segmentation, where user can provide multi-class seed pixels instead of just foreground background 2-class seeds, for segmenting the given image into the desired number of regions by performing a one-shot optimization operation, which again has a unique global minimum and can be obtained by solving a large system of linear equations. We present various experimental results, including segmentation error rates on an online image database with human labeled ground truth, to show that our method works well and has direct applications in areas such as interactive image editing. 1