Object Recognition from Large Structural Libraries

This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure commences from a Bayesian consistency criterion which as been developed for locating correspondence matches between attributed relational graphs using iterative relaxation operations. The aim in this paper is to simplify the consistency measure so that it may used in a non-iterative manner without the need to compute explicit correspondence matches. This considerably reduces the computational overheads and renders the consistency measure suitable for large-scale object recognition. The measure uses robust error-kernels to gauge the similarity of pairwise attribute relations defined on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 2000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 94%. A comparative study reveals that the method is most effective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdorff distance.