  Over-segmentation of an image into superpixels has become a useful tool for
solving various problems in image processing and computer vision. Reflection
symmetry is quite prevalent in both natural and man-made objects and is an
essential cue in understanding and grouping the objects in natural scenes.
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 first find
the reflection symmetry in the image and represent it by a set of pairs of
pixels which are mirror reflections of each other. We partition the image into
superpixels while preserving this reflection symmetry through an iterative
algorithm. We compare the proposed method with state-of-the-art superpixel
generation methods and show the effectiveness in preserving the size and shape
of superpixel boundaries across the reflection symmetry axes. We also present
two applications, symmetry axes detection and unsupervised symmetric object
segmentation, to illustrate the effectiveness of the proposed approach.
