Multiregion Image Segmentation by Parametric Kernel Graph Cuts

Abstract—The purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data. The image data is transformed implicitly by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable. The objective function contains an original data term to evaluate the deviation of the transformed data, within each segmentation region, from the piecewise constant model, and a smoothness, boundary preserving regularization term. The method affords an effective alternative to complex modeling of the original image data while taking advantage of the computational benefits of graph cuts. Using a common kernel function, energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation. A quantitative and comparative performance assessment is carried out over a large number of experiments using synthetic grey level d...

• Type: Journal
• Year: 2011
• Where: TIP
• Authors: Mohamed Ben Salah, Amar Mitiche, Ismail Ben Ayed
• Keywords: Image Data, Images, Piecewise Constant Model, TIP 2011