Open AccessSnakeCut: An Integrated Approach Based on Active Contour and GrabCut for Automatic Foreground Object Segmentation
Author(s) -
Surya Prakash,
R. Abhilash,
Sukhendu Das
Publication year - 2007
Publication title -
elcvia. electronic letters on computer vision and image analysis
Language(s) - English
Resource type - Journals
ISSN - 1577-5097
DOI - 10.5565/rev/elcvia.139
Subject(s) - artificial intelligence , computer vision , computer science , segmentation , cut , active contour model , object (grammar) , image segmentation , pixel , segmentation based object categorization , scale space segmentation , graph , pattern recognition (psychology) , theoretical computer science
Interactive techniques for extracting the foreground object from an image have been the interest of research in computer vision for a long time. This paper addresses the problem of an efficient, semi-interactive extraction of a foreground object from an image. Snake (also known as Active contour) and GrabCut are two popular techniques, extensively used for this task. Active contour is a deformable contour, which segments the object using boundary discontinuities by minimizing the energy function associated with the contour. GrabCut provides a convenient way to encode color features as segmentation cues to obtain foreground segmentation from local pixel similarities using modified iterated graph-cuts. This paper first presents a comparative study of these two segmentation techniques, and illustrates conditions under which either or both of them fail. We then propose a novel formulation for integrating these two complimentary techniques to obtain an automatic foreground object segmentation. We call our proposed integrated approach as “SnakeCut”, which is based on a probabilistic framework. To validate our approach, we show results both on simulated and natural images