Shape automatic clustering-based multi-objective optimization with decomposition

Shape automatic clustering-based multi-objective optimization with decomposition 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

Shape automatic clustering-based multi-objective optimization with decomposition

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0850-6
Publisher site
See Article on Publisher Site

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.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Jul 4, 2017

References

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