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Retrieving Similar Shapes Effectively and Efficiently

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Abstract

In this paper, we address the following problem: given a large collection of shapes and a query shape, retrieve all shapes (from the shape database) that are similar to the query shape. A generalized centroid-radii model is used to model all forms of shapes — convex shapes, concave shapes and shapes with “holes”. Under the model, a shape is represented by a set of vectors, each obtained from the radii emanating from the centroid of a virtual concentric ring.

The model can also facilitate multi-resolution and similarity retrievals. Furthermore, using the model, the shape of an object can be transformed into a point in a high dimensional data space. To speed up the retrieval of similar shapes, we also propose a multi-level R-tree index, called the Nested R-trees (NR-trees). Unlike traditional high-dimensional index structures that index a high-dimensional point as it is (with its full dimension), the NR-trees splits the dimensionality of the point into a set of lower dimensions that are indexed by levels of the NR-trees. We also proposed a quick filtering mechanism to further prune the search space.

We implemented a shape retrieval system that employs the generalized centroid-radii model and the NR-trees with the filtering mechanism. Our experimental study shows the effectiveness of the proposed shape model, and the efficiency of the NR-trees. The results also show that the filtering mechanism can significantly reduce the retrieval time.

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  1. Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore, 117543

    Kian-Lee Tan, Beng Chin Ooi & Lay Foo Thiang

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  1. Kian-Lee Tan
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Tan, KL., Ooi, B.C. & Thiang, L.F. Retrieving Similar Shapes Effectively and Efficiently. Multimedia Tools and Applications 19, 111–134 (2003). https://doi.org/10.1023/A:1022142527536

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