Fast Shape Matching Using Statistical Features of Shape Contexts

Moon-Jai LIM  Chan-Hee HAN  Si-Woong LEE  Yun-Ho KO  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.10   pp.2056-2058
Publication Date: 2011/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.2056
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
shape context,  generalized shape context,  shape matching,  fast algorithm,  

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A novel fast algorithm for shape matching using statistical features of shape contexts is presented. By pruning the candidate shapes using the moment-based statistical features of shape contexts, the required number of matching processes is dramatically reduced with negligible performance degradation. Experimental results demonstrate that the proposed algorithm reduces the pruning time up to 1/(r·n) compared with the conventional RSC algorithm while maintaining a similar or better performance, where n is the number of sampled points of a shape and r is the number of randomly selected representative shape contexts for the query shape.