Improved spectral matting by iterative K-means clustering and the modularity measure

Spectral matting is a useful technique for image matting problem. A crucial issue of spectral matting is to determine the number of matting components which has large impacts on the matting performance. In this paper, we propose an improved framework based on spectral matting in order to solve this...

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Bibliographic Details
Main Authors: Tung-Yu Wu, Hung-Hui Juan, Lu, H. H-S
Format: Conference Proceeding
Language:English
Subjects:
Equations
Image color analysis
Image matting
Image segmentation
Iterative methods
Laplace equations
Modularity
Optimization
Spectral matting
Vectors
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Summary:Spectral matting is a useful technique for image matting problem. A crucial issue of spectral matting is to determine the number of matting components which has large impacts on the matting performance. In this paper, we propose an improved framework based on spectral matting in order to solve this limitation. Iterative K-means clustering with the assistance of the modularity measure is adopted to obtain the hard segmentation that can be used as the initial guess of soft matting components. The number of matting components can be determined automatically because the improved framework will search possible image components by iteratively dividing image subgraphs.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2012.6288094