Improving Sample Optimization with Convergence Speed Controller for Sampling-Based Image Matting

Image matting is a core and challenging operator when processing images or videos. Its aim is to accurately extract the foreground region from an image. In this paper, we explore sampling-based image matting. The key optimization problem of sampling-based image matting is how to search the best foreground-background sample pair for every undetermined pixel. It is termed as the “sample optimization problem”. Many sample optimization algorithms have been proposed for improving the efficiency of searching the best foreground-background sample pair. However, they fail when premature convergence is occurred. This paper presents a new sample optimization algorithm, which is based on convergence speed controller (CSC). The CSC is a general algorithm strategy. It can be embedded into algorithms and enhance the performance of the algorithms by maintaining the convergence speed and preventing premature convergence. By comparing with existing sample optimization algorithms, the experimental results show that our algorithm is competitive and effective to search the best sample pair and improve the performance of sampling-based image matting.