SymmSLIC: Symmetry Aware Superpixel Segmentation and its Applications

TL;DR

SymmSLIC introduces a superpixel segmentation method that preserves reflection symmetry in images, improving boundary consistency across symmetry axes and enabling applications like symmetry detection and symmetric object segmentation.

Contribution

The paper presents a novel superpixel algorithm that explicitly incorporates reflection symmetry, addressing limitations of existing methods in symmetry preservation.

Findings

Effectively preserves superpixel boundary symmetry across axes

Outperforms state-of-the-art methods in symmetry-aware segmentation

Enables accurate symmetry axis detection and symmetric object segmentation

Abstract

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.