Interactive Image Segmentation via Graph Clustering and Synthetic Coordinates Modeling

Costas Panagiotakis (1) , Harris Papadakis (2) , Elias Grinias , Nikos Komodakis (3, 4, 5) , Paraskevi Fragopoulou (2) , Georgios Tziritas (1)

1 CSD-UOC - Computer Science Department [Crete]
2 ICS-FORTH - Institute of Computer Science [FORTH, Heraklion]
3 CVN - Centre de vision numérique
4 ENPC - École des Ponts ParisTech
5 LIGM - Laboratoire d'Informatique Gaspard-Monge

We propose a method for interactive image segmentation. We construct a weighted graph that represents the superpixels and the connections between them. An efficient algorithm for graph clustering based on synthetic coordinates is used yielding an initial map of classified pixels. The proposed method minimizes a min-max Bayesian criterion that has been successfully used on image segmentation problem taking into account visual information as well as the given markers. Experimental results and comparisons with other methods demonstrate the high performance of the proposed scheme.

Keywords: image segmentation; network coordinates; graph clustering

Domains: Computer Vision and Pattern Recognition [cs.CV]

Costas Panagiotakis, Harris Papadakis, Elias Grinias, Nikos Komodakis, Paraskevi Fragopoulou, et al.. Interactive Image Segmentation via Graph Clustering and Synthetic Coordinates Modeling. 15th International Conference on Computer Analysis of Images and Patterns (CAIP 2013), Aug 2013, York, United Kingdom. pp.589-596, ⟨10.1007/978-3-642-40261-6_71⟩. ⟨hal-00865703⟩