Spectral matting

Anat Levin*, Alex Rav-Acha, Dani Lischinski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

262 Scopus citations


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.

Original languageAmerican English
Pages (from-to)1699-1712
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - 2008


  • Image matting
  • Spectral analysis
  • Unsupervised segmentation


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