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Smooth Representation Clustering Based on Kernelized Random Walks

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10612))

Abstract

With the widespread use of smart phones and tablet computers, it is necessary to develop algorithms to assist high throughout analysis of mobile videos. A novel method for automated segmentation on the mobile video scenery is proposed in this paper. It uses the kernelized random walks on the globe KNN graph and the Smooth Representation Clustering to improve the segmentation effectiveness. The high order transition probability matrix of the kernelized random walks is utilized for erasing the unreliable edge of the graph. Simultaneously kernel approach is used to assign different weights for neighbors to evaluate their contribution to the clustering. The method is evaluated on two public datasets and a real-world mobile video taken by a smart phone. The experimental results show that the proposed algorithm achieves better performance compared with the other representative algorithms.

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Acknowledgments

This work was supported by Chinese National Natural Science Foundation under Grant Nos. 61672157, 41601477, it is also supported by the Leading project in Science and Technology Department of Fujian Province under Grant No. 2015Y0054.

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Authors and Affiliations

  1. School of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350007, People’s Republic of China

    Liping Chen, Gongde Guo & Lifei Chen

Authors
  1. Liping Chen
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  2. Gongde Guo
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  3. Lifei Chen
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Corresponding author

Correspondence to Liping Chen .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Shaoxu Song

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Chen, L., Guo, G., Chen, L. (2017). Smooth Representation Clustering Based on Kernelized Random Walks. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-69781-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69780-2

  • Online ISBN: 978-3-319-69781-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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