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Generalization Accuracy of a Fast 2-D Shape Retrieval Method

In recent work, we presented a correspondence-based approach for real-time retrieval from databases of 2-D shapes. The system performs with high speed and accuracy relative to other published systems as measured on a widely used benchmark dataset. In this paper, we test the generalization accuracy of the approach; i.e., its ability to correctly retrieve shapes from a shape database when the query shapes are not contained in the database. The experimental results show that the system's accuracy on novel inputs is nearly as high as its accuracy on shapes that are already contained in the database, demonstrating that the approach has good generalization accuracy. This result provides additional support for the use of the proposed approach for shape retrieval and object recognition applications.