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Graph-based shape indexing

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Abstract

Graphs have become growingly important in representing shapes in computer vision. Given a query graph, it is essential to retrieve similar database graphs efficiently from a large database. In this paper, we present a graph-based indexing technique which overcomes significant drawbacks of the previous work (Demirci et al. in Comput Vis Image Underst 110(3):312–325, 2008) using a recently developed theorem from the domain of matrix analysis. Our technique starts by representing the topological structure of a graph in a vector space. As done in the previous work, the topological structure of a graph is constructed using its Laplacian spectra. However, unlike the previous approach, which represents all sugraphs of a database graph in the vector space to account for local similarity, a database graph in the proposed framework is represented as a single vector. By performing a range search around the query, the proposed indexing technique returns a set with both partial and global similarity. Empirical evaluation of the algorithm on an extensive set of retrieval trials including a comparison with the previous approach in both 2D and 3D demonstrates the effectiveness, efficiency, and robustness of the overall approach.

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  1. Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey

    M. Fatih Demirci

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  1. M. Fatih Demirci
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Demirci, M.F. Graph-based shape indexing. Machine Vision and Applications 23, 541–555 (2012). https://doi.org/10.1007/s00138-010-0290-z

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