Symmetry integrated region-based image segmentation

Abstract

Symmetry is an important cue for machine perception that involves high-level knowledge of image components. Unlike most of the previous research that only computes symmetry in an image, this paper integrates symmetry with image segmentation to improve the segmentation performance. The symmetry integration is used to optimize both the segmentation and the symmetry of regions simultaneously. Interesting points are initially extracted from an image and they are further refined for detecting symmetry axis. A symmetry affinity matrix is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of segmented regions. Experimental results and comparisons from a wide domain of images indicate a promising improvement by symmetry integrated image segmentation compared to other image segmentation methods that do not exploit symmetry.