Volume III-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 387-394, 2016
https://doi.org/10.5194/isprs-annals-III-3-387-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 387-394, 2016
https://doi.org/10.5194/isprs-annals-III-3-387-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

SUPERPIXEL CUT FOR FIGURE-GROUND IMAGE SEGMENTATION

Michael Ying Yang1 and Bodo Rosenhahn2 Michael Ying Yang and Bodo Rosenhahn
  • 1University of Twente, ITC Faculty, EOS department, Enschede, the Netherlands
  • 2Leibniz University Hannover, Institute of Information Processing, Germany

Keywords: Computer Vision, Superpixel Cut, Min-Cut, Image Segmentation

Abstract. Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.