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Unsupervised Shape Co-segmentation Based on Transformation Network

  • Research Article - Computer Engineering and Computer Science
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Abstract

Unsupervised co-segmentation is one type of shape segmentation. It segments a set of 3D shapes into meaningful parts and creates a correspondence between parts simultaneously without any labeled data. Clustering-based co-segmentation is based on the correlation analysis in a descriptor space and has received increasing attention. In this paper, we propose a co-segmentation method, in which a transformation network for data representation is trained by extreme learning machine, embedding shape primitives into more discriminant feature spaces, so as to achieve better segmentation performance. Thus, co-segmentation can be implemented by clustering on lower dimensions based on the transformation network, so the execution is more efficient. Moreover, once the transformation network is trained, it can be applied to the data representation acquisition process without re-computing similarity parameters. In order to create and train the transformation network, the correlation of shape primitives is utilized. Therefore, an affinity matrix construction method based on parameter-free and high-efficiency simplex sparse representation is introduced. This construction of correlation avoids the blindness of parameter setting. Experimental results show that the proposed co-segmentation method is effective and efficient. In addition, it also can deal with incremental co-segmentation when the dataset is expanded.

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and suggestions. And thanks to all the people who have supported this paper.

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Authors and Affiliations

  1. Institute of Computing and Software, Nanjing Vocational College of Information Technology, Nanjing, People’s Republic of China

    Hongyan Li

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, People’s Republic of China

    Hongyan Li & Zhengxing Sun

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  1. Hongyan Li
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  2. Zhengxing Sun
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Correspondence to Hongyan Li.

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The authors declares that they have no conflict of interest.

Additional information

This work was supported by Key Natural Science Fund of Nanjing Vocational College of Information Technology (No. YK20170401), National High Technology Research and Development Program of China (No. 2007AA01Z334), National Natural Science Foundation of China (Nos. 61321491 and 61272219), Innovation Fund of State Key Laboratory for Novel Software Technology (Nos. ZZKT2013A12 and ZZKT2016A11), Program for New Century Excellent Talents in University of China (NCET-04-04605), and the University Science Research Project of Jiangsu Province (Grant No. 17KJB520013).

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Li, H., Sun, Z. Unsupervised Shape Co-segmentation Based on Transformation Network. Arab J Sci Eng 44, 9029–9041 (2019). https://doi.org/10.1007/s13369-019-04015-1

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