Author :
Levin, Anat ; Rav-Acha, Alex ; Lischinski, Dani
Author_Institution :
MIT, Cambridge, MA
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.
Keywords :
Laplace equations; eigenvalues and eigenfunctions; feature extraction; fuzzy set theory; image segmentation; matrix algebra; Laplacian matrix; eigenvectors; fuzzy matting components; natural image matting; spectral matting; spectral segmentation techniques; image segmentation; matting; spectral analysis; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2008.168
