Biological Inspired Global Descriptor for Shape Matching.
ABSTRACT Shape description is the precondition for shape matching and retrieval. The more robust and stable primitives to describe
shapes are global topological properties, but obtaining global topological properties is still an obstacle in computer vision.
Motivated by the difference sensitivity of short-range connection in biology vision, we present a novel global descriptor
to describe the entire topology of simple closed 2D shape in this paper. We employ two novel strategies – the zigzag rule,
which approximates shape to an elaborate polygonal curve, and cost function which combines global configurations as well as
local information of the line stimulations as our punishments. With these two key steps the descriptor is robust to translation,
scaling and rotation. Experimental results show the model gain good performance on matching and retrieval for silhouettes.
Even for images with occlusion the result is excellent and reasonable.