Fast and Adaptive Pairwise Similarities for Graph Cuts-based Image Segmentation

Baris Sumengen
UC, Santa Barbara
Santa Barbara, CA 93106
sumengen [at] ece.ucsb.edu

Luca Bertelli
UC, Santa Barbara
Santa Barbara, CA 93106
lbertelli [at] ece.ucsb.edu

B. S. Manjunath
UC, Santa Barbara
Santa Barbara, CA 93106
manj [at] ece.ucsb.edu

Abstract

We introduce the use of geodesic distances and geodesic radius for calculating pairwise similarities needed in various graph cuts based methods. By using geodesics on an edge strength function we are able to calculate similarities between pixels in a more natural way. Our technique improves the speed and reliability of calculating similarities and leads to reasonably good image segmentation results. Our algorithm takes an edge strength function as its input and its speed is independent of the feature dimension or the distance measure used.
[PDF] [BibTex]
Baris Sumengen, Luca Bertelli and B.S. Manjunath,
IEEE CVPR Workshop on Perceptual Organization in Computer Vision (POCV), New York City, Jun. 2006.
Node ID: 432 , DB ID: 235 , VRLID: 156 , Lab: VRL , Target: Proceedings