Fast Image matting based on matting pyramids and surrogate models
Yihui Liang, Hongshan Gou, Guisong Liu, Sibo Huang · 2023
Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time.