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Efficient shape classification using region descriptors

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Abstract

A novel scheme for efficient shape classification using region descriptors and extreme learning machine with kernels is proposed. The skeleton and boundary of the input shape image are first extracted. Then the boundary is simplified to remove noise and minor variations. Finally, region descriptors for the local skeleton, and the simplified shape signature are constructed to form a hybrid feature vector. Training and classification are then performed using kernel extreme learning machine (k-ELM) for efficient shape classification. Experimental results show that the proposed scheme is very fast and can archive high classification accuracy of 91.43 % on the challenging MPEG-7 dataset, outperforming existing state-of-the-art methods.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This research was supported in part by the Research Committee of the University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST) and the Science and Technology Development Fund of Macau SAR (008/2013/A1, 093-2014-A2).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Cong Lin, Chi-Man Pun & Chi-Man Vong

  2. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26508, USA

    Don Adjeroh

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  1. Cong Lin
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  2. Chi-Man Pun
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  3. Chi-Man Vong
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  4. Don Adjeroh
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Correspondence to Chi-Man Pun.

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Lin, C., Pun, CM., Vong, CM. et al. Efficient shape classification using region descriptors. Multimed Tools Appl 76, 83–102 (2017). https://doi.org/10.1007/s11042-015-3021-7

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  • DOI: https://doi.org/10.1007/s11042-015-3021-7

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