  Symmetry is an important composition feature by investigating similar sides
inside an image plane. It has a crucial effect to recognize man-made or nature
objects within the universe. Recent symmetry detection approaches used a
smoothing kernel over different voting maps in the polar coordinate system to
detect symmetry peaks, which split the regions of symmetry axis candidates in
inefficient way. We propose a reliable voting representation based on weighted
linear-directional kernel density estimation, to detect multiple symmetries
over challenging real-world and synthetic images. Experimental evaluation on
two public datasets demonstrates the superior performance of the proposed
algorithm to detect global symmetry axes respect to the major image shapes.
