Efficient graph cuts for multiclass interactive image segmentation

Authors:
Fangfang Lu;Zhouyu Fu;Antonio Robles-Kelly
Affiliations:
Research School of Information Sciences and Engineering, Australian National University;Research School of Information Sciences and Engineering, Australian National University;Research School of Information Sciences and Engineering, Australian National University and National ICT Australia, Canberra Research Laboratory, Canberra, Australia
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Year:
2007

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Abstract

Interactive Image Segmentation has attracted much attention in the vision and graphics community recently. A typical application for interactive image segmentation is foreground/background segmentation based on user specified brush labellings. The problem can be formulated within the binary Markov Random Field (MRF) framework which can be solved efficiently via graph cut [1]. However, no attempt has yet been made to handle segmentation of multiple regions using graph cuts. In this paper, we propose a multiclass interactive image segmentation algorithm based on the Potts MRF model. Following [2], this can be converted to a multiway cut problem first proposed in [2] and solved by expansion-move algorithms for approximate inference [2]. A faster algorithm is proposed in this paper for efficient solution of the multiway cut problem based on partial optimal labeling. To achieve this, we combine the one-vs-all classifier fusion framework with the expansion-move algorithm for label inference over large images. We justify our approach with both theoretical analysis and experimental validation.