A two-stage framework for polygon retrieval
We propose a two-stage framework for polygon retrieval which incorporates both qualitative and quantitative measures of polygons in the first and second stage respectively. The first stage uses Binary Shape Descriptor as a mean to prune the search space. The second stage uses any available polygon matching and similarity measuring technique to compare model polygons with the target polygon. This two-stage framework uses a combination of model-driven approach and data-driven approach. It is more efficient than model-driven approach since it reduces the number of polygons needed to be compared. By using binary string as index, it also avoids the difficulty and inefficiency of manipulating complex multi-dimensional index structure. This two-stage framework can be incorporated into image database systems for providing query-by-shape facility. We also propose two similarity measures for polygons, namely Multi-Resolution Area Matching and Minimum Circular Error Bound, which can be used in the second stage of the two-stage framework. We compare these two techniques with the Hausdorff Distance method and the Normalized Coordinate System method. Our experiments show that Multi-Resolution Area Matching technique is more efficient than the two methods and Minimum Circular Error Bound technique produces better polygon similarity measure than the two methods.
polygon matching image database content-based retrieval