SymmSLIC: Symmetry Aware Superpixel Segmentation

Over-segmentation of an image into superpixels has become an useful tool for solving various problems in computer vision. Reflection symmetry is quite prevalent in both natural and man-made objects. Existing algorithms for estimating superpixels do not preserve the reflection symmetry of an object which leads to different sizes and shapes of superpixels across the symmetry axis. In this work, we propose an algorithm to over-segment an image through the propagation of reflection symmetry evident at the pixel level to superpixel boundaries. In order to achieve this goal, we exploit the detection of a set of pairs of pixels which are mirror reflections of each other. We partition the image into superpixels while preserving this reflection symmetry information through an iterative algorithm. We compare the proposed method with state-of-the-art superpixel generation methods and show the effectiveness of the method in preserving the size and shape of superpixel boundaries across the reflection symmetry axes. We also present an application called unsupervised symmetric object segmentation to illustrate the effectiveness of the proposed approach.