Biological Inspired Global Descriptor for Shape Matching

Authors:
Yan Li;Siwei Luo;Qi Zou
Affiliations:
Department of Computer Science Beijing Jiao Tong University, Postcode 100044 Beijing, China;Department of Computer Science Beijing Jiao Tong University, Postcode 100044 Beijing, China;Department of Computer Science Beijing Jiao Tong University, Postcode 100044 Beijing, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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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.