Guan, YuChen, WeiLiang, XiaoDing, ZiangPeng, Qunsheng2015-02-212015-02-2120061467-8659https://doi.org/10.1111/j.1467-8659.2006.00976.xWe propose an iterative energy minimization framework for interactive image matting. Our approach is easy in the sense that it is fast and requires only few user-specified strokes for marking the foreground and background. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy in each iteration as the combination of one data term and one smoothness term. By automatically adjusting the weights of both terms during the iterations, the first-order continuous and feature-preserving result is rapidly obtained with several iterations. The energy optimization can be further performed in selected local regions for refined results. We demonstrate that our energy-driven scheme can be extended to video matting, with which the spatio-temporal smoothness is faithfully preserved. We show that the proposed approach outperforms previous methods in terms of both the quality and performance for quite challenging examples.10.1111/j.1467-8659.2006.00976.x567-576