Automatic framework for highly efficient natural image matting
Fazhi He, Yue Wu, Dengyi Zhang, Zhiyong Huang, Lingyun Wei, Chunxia Xiao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Traditional image matting approaches requires user interaction. This paper proposes an automatic framework for natural image matting. The method seamlessly incorporates image matting with the top-down process of segmentation by weighted aggregation to get a rich and multi-scale grapy pyramid representation of the input image. Using the coupling between aggregates in the graph pyramid, the region for matting is detected adaptively and automatically. Meanwhile, foreground and background regions are determined with state variables. An energy function is constructed to represent the similarity and smoothness properties of a matte and is iteratively optimized. Under the automatic matting framework, color sampling is more accurate than existing methods since multi-scale measurements such as intensity and texture are fully considered. Experiments show that the proposed automatic method is more efficient to extract high quality matte even for difficult images in which foreground and background have very similar colors. Another attractive feature of the method is that it can extract mattes for multi-objects at one computing time.