Interactive image segmentation by constrained spectral graph partitioning
Hao Zhang, Jin He, Hong Zhang, Zhanhua Huang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
This paper proposed an interactive image segmentation algorithm that can tolerate slightly incorrect user constraints. Interactive image segmentation was formulated as a constrained spectral graph partitioning problem. Furthermore, it was proven to equal to a supervised classification problem, where the feature space was formed by rows of the eigenvector matrix that was computed by spectral graph analysis. ν-SVM (support vector machine) was preferred as the classifier. Some incorrect labels in user constraints were tolerated by being identified as margin errors in ν-SVM. Comparison with other algorithms on real color images was reported.