title: Spectral matting.
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.
authors.affiliation: MIT CSAIL, The State Center 32-D466, Cambridge, MA 02139, USA. alevin@csail.mit.edu
authors.forename: Anat
authors.initials: A
authors.lastname: Levin
authors.forename: Alex
authors.initials: A
authors.lastname: Rav-Acha
authors.forename: Dani
authors.initials: D
authors.lastname: Lischinski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique