Background Matting
This work addresses image and video matting for computer vision applications, representing an incremental improvement by modifying an existing method.
This paper tackles the problem of alpha matting by incorporating background information alongside the traditional trimap guidance, achieving significant performance improvements over state-of-the-art methods on the Adobe Composition-1k dataset.
The current state of the art alpha matting methods mainly rely on the trimap as the secondary and only guidance to estimate alpha. This paper investigates the effects of utilising the background information as well as trimap in the process of alpha calculation. To achieve this goal, a state of the art method, AlphaGan is adopted and modified to process the background information as an extra input channel. Extensive experiments are performed to analyse the effect of the background information in image and video matting such as training with mildly and heavily distorted backgrounds. Based on the quantitative evaluations performed on Adobe Composition-1k dataset, the proposed pipeline significantly outperforms the state of the art methods using AlphaMatting benchmark metrics.