Fuzzy Relational Distance for Large-Scale Object Recognition

This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure draws its inspiration from both the Hausdor distance and a recently reported Bayesian consistency measure that has been sucessfully used for graphbased correspondence matching. The measure uses robust error-kernels to gauge the similarity of pairwise attribute relations de ned on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 1000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 98%. A comparative study reveals that the method is most e ective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdor distance.