Object segmentation using graph cuts based active contours

Ning Xu, Narendra Ahuja, Ravi Bansal

Research output: Contribution to journalArticlepeer-review


In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation. GCBAC approach is a combination of the iterative deformation idea of active contours and the optimization tool of graph cuts. It differs from traditional active contours in that it uses graph cuts to iteratively deform the contour and its cost function is defined as the summation of edge weights on the cut. The resulting contour at each iteration is the global optimum within a contour neighborhood (CN) of the previous result. Since this iterative algorithm is shown to converge, the final contour is the global optimum within its own CN. The use of contour neighborhood alleviates the well-known bias of the minimum cut in favor of a shorter boundary. GCBAC approach easily extends to the segmentation of three and higher dimensional objects, and is suitable for interactive correction. Experimental results on selected data sets and performance analysis are provided.

Original languageEnglish (US)
Pages (from-to)210-224
Number of pages15
JournalComputer Vision and Image Understanding
Issue number3
StatePublished - Sep 2007


  • Active contours
  • Graph cut
  • Object segmentation
  • Snakes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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