Interactive image segmentation by constrained spectral graph partitioning

Hao Zhang; Jin He; Hong Zhang; Zhanhua Huang

doi:10.1117/12.870254

10 November 2010 Interactive image segmentation by constrained spectral graph partitioning

Hao Zhang, Jin He, Hong Zhang, Zhanhua Huang

Proceedings Volume 7850, Optoelectronic Imaging and Multimedia Technology; 78501X (2010) https://doi.org/10.1117/12.870254
Event: Photonics Asia 2010, 2010, Beijing, China

Abstract

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.

Citation Download Citation

Hao Zhang, Jin He, Hong Zhang, and Zhanhua Huang "Interactive image segmentation by constrained spectral graph partitioning", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 78501X (10 November 2010); https://doi.org/10.1117/12.870254

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