A Level Set Model for Image Classification

  • Authors:
  • Christophe Samson;Laure Blanc-Féraud;Gilles Aubert;Josiane Zerubia

  • Affiliations:
  • -;-;-;-

  • Venue:
  • SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
  • Year:
  • 1999

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a supervised Classification model based on a variational approach. This model is devoted to find an optimal partition compound of homogeneous classes with regular interfaces. We represent the regions of the image defined by the classes and their interfaces by level set functions, and we define a functional whose minimum is an optimal partition. The coupled Partial Differential Equations (PDE) related to the minimization of the functional are considered through a dynamical scheme. Given an initial interface set (zero level set), the Different terms of the PDE's are governing the motion of interfaces such that, at convergence, we get an optimal partition as defined above. Each interface is guided by internal forces (regularity of the interface), and external ones (data term, no vacuum, no regions overlapping). Several experiments were conducted on both synthetic an real images.