### Abstract

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.

»The Paper (PDF)

»A Technical Report with some more details (PDF)

»Supplementary Material (PDF)

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### The spectral matting process:

**Input Image**
**Smallest eigenvectors of the matting Laplacian matrix**
**Matting components - linear combinations of smallest eigenvectors**
**Foreground matte extracted by adding matting components**
**Comparison- hard segmentation**