A map search system using wavelet and shape contexts

This paper presents a map search system using Shape context and Bipartite graph matching. Shape context is using for measuring shape similarity and the recovering of point correspondences. After generated from the shape context, Bipartite Graph Matching leads the optimal correspondence between two shapes. For raising the recognition rate, Hierarchical description is used. Shape context is the method that treats shapes as a set of points and makes the histogram of the distribution of points. In Hierarchical description, Wavelet analysis is used. To reduce the amount of calculation, Piecewise linear approximation is implemented as the feature extraction method. The system shows six similar shapes to hand-written input shapes from reference shapes that are Japan's 47 prefectures. Comparison result of Linear matching, DP matching and Shape context with Bipartite graph matching describes that the 1st place recognition rates of each algorithms are 82.00%, 84.52% and 92.45%, which describes the robustness of Shape context. Experiment of several level of hierarchical description describes that hierarchical approximation can raise the recognition rate from 92.45 to 94.97% using the deepest-4 depth, which means that the hierarchical approximation is effective to remove the noise from the handwritten inputs.