Coarse Trimap Expansion Based on One‐class Classification for Image Matting

Trimap is required for most image matting algorithms, as it provides partial regions with known opacity. As capturing elaborate trimaps is a time-consuming process, in practice, users prefer to provide coarse trimaps with large unknown regions. However, extant image matting algorithms cannot provide high-quality alpha mattes based on coarse trimaps. Although some matting algorithms include trimap expansion in the pre-processing stage, if this is done by directly comparing the similarity of image features between pixels, errors and omissions can easily occur. To overcome this issue, in this paper, a coarse trimap expansion model based on one-class classification is presented, in which the problem is treated as a process of reclassifying pixels in unknown regions. For this purpose, a coarse trimap expansion method denoted as CTE-OC is proposed, in which the similarity between pixels is reliably determined by measuring semantic features, allowing newly developed one-class classifiers to adequately classify pixels in entire unknown regions. The validity of these strategies is tested experimentally, and the results show that CTE-OC can significantly improve the quality of alpha mattes obtained by extant image matting methods when provided with coarse trimaps.