Spectral Matting

We present spectral matting: a new approach to natural image matting that automatically computes a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. These components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Anat Levin, Alex Rav-Acha, Dani Lischinski
• Keywords: Computer Vision, CVPR 2007, Fuzzy Matting Components, Meaningful Foreground, Smallest Eigenvectors, Soft Matting Components, Spectral Segmentation Techniques