  Symmetry is one of the significant visual properties inside an image plane,
to identify the geometrically balanced structures through real-world objects.
Existing symmetry detection methods rely on descriptors of the local image
features and their neighborhood behavior, resulting incomplete symmetrical axis
candidates to discover the mirror similarities on a global scale. In this
paper, we propose a new reflection symmetry detection scheme, based on a
reliable edge-based feature extraction using Log-Gabor filters, plus an
efficient voting scheme parameterized by their corresponding textural and color
neighborhood information. Experimental evaluation on four single-case and three
multiple-case symmetry detection datasets validates the superior achievement of
the proposed work to find global symmetries inside an image.
