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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
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Several recently published shape retrieval systems have achieved high accuracy on a benchmark 1400-shape dataset. However, some of these systems have high pairwise shape matching costs due to their use of structural matching or flexible correspondence. The purpose of this paper is to demonstrate that a relatively simple shape retrieval system based on fixed point correspondences can achieve accuracy comparable with the two most accurate prior systems, at significantly higher speed. High accuracy is achieved by using a stable curve normalization procedure and example-based retrieval. High speed is achieved by three techniques: single key-point alignment, fixed correspondences, and PCA-based dimensionality reduction. The system is completely automatic and does not require that the database shapes have class labels.