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3D shape clustering with Nonnegative Least Squares coding and fusion on multilayer graphs

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Abstract

The well-known method of spectral clustering for 3D shape datasets is revisited. The graph construction by NNLS (Nonnegative Least Squares) coding technique is at the core of our method. Using graph-based encoding techniques and well-known shape descriptors, a framework is presented here which is applied to 3D shape clustering. Provided a shape database, a graph is first constructed. Weights in the graph are calculated so as to approximate each shape as a sparse linear combination of the remaining dataset objects. This framework is further enhanced by using the multilayer graphs process combining NNLS with L2graph. The L2graph is a sparse similarity graph, which conveys complementary information to NNLS coding. A criterion for the complementary action of the two graphs in terms of graph distance is also presented. Experimental results conducted on SHREC10, SHREC11 and SCHREC15 datasets validate the excellent performance of our clustering framework.

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Authors and Affiliations

  1. Department of Computer Engineering and Informatics, University of Patras, 26500, Patras, Greece

    Foteini Fotopoulou

  2. Department of Physics, Electronics Laboratory, University of Patras, 26500, Patras, Greece

    George Economou

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  1. Foteini Fotopoulou
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  2. George Economou
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Correspondence to Foteini Fotopoulou.

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Fotopoulou, F., Economou, G. 3D shape clustering with Nonnegative Least Squares coding and fusion on multilayer graphs. Multimed Tools Appl 79, 32607–32622 (2020). https://doi.org/10.1007/s11042-020-09668-x

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  • DOI: https://doi.org/10.1007/s11042-020-09668-x

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