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Shape automatic clustering-based multi-objective optimization with decomposition

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Abstract

In this paper, a new shape automatic clustering method based on multi-objective optimization with decomposition (MOEA/D-SAC) is proposed, which aims to find the final cluster number k as well as an optimal clustering result for the shape datasets. Firstly, an improved shape descriptor based on the shape context is proposed to measure the distance between shapes. Secondly, the diffusion process is applied to transform the similarity distance matrix among total shapes of a dataset into a weighted graph, where the shapes and their distance are regarded as nodes and weight of edges, respectively. Thirdly, a new clustering method called “the soft clustering” is devised, starting with constructing an adjacency graph which can maintain the edges with the weights of k-nearest-neighbor nodes. Then, a multi-objective evolutionary algorithm with decomposition (MOEA/D) is applied to achieve automatic graph clustering scheme. The proposed clustering algorithm has been used to cluster several shape datasets, including four kimia datasets and a well-known MPEG-7 dataset, and experimental results show that the proposed method can demonstrate competitive clustering results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61373111), the Fundamental Research Funds for the Central University (Nos. K50511020014, K5051302084) and the Provincial Natural Science Foundation of Shaanxi of China (No. 2014JM8321).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Ruinan Wang, Xin Yu & Lijia An

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  1. Ruochen Liu
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  2. Ruinan Wang
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  3. Xin Yu
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  4. Lijia An
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Correspondence to Ruochen Liu.

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Liu, R., Wang, R., Yu, X. et al. Shape automatic clustering-based multi-objective optimization with decomposition. Machine Vision and Applications 28, 497–508 (2017). https://doi.org/10.1007/s00138-017-0850-6

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