Spectral Matting

Authors:
Anat Levin;Alex Rav-Acha;Dani Lischinski
Affiliations:
MIT, cambridge;The Hebrew University of Jerusalem, Jerusalem;The Hebrew University of Jerusalem, Jerusalem
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2008

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Abstract

We present spectral matting: a new approach to natural image matting that automatically computes a basis set of 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.