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Foundations and Applications of Intelligent Systems pp 579–589Cite as

Adaptive Boosting for Enhanced Vortex Visualization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 213))

Abstract

In this paper, we demonstrate the use of machine learning techniques to enhance the robustness of vortex visualization algorithms. We combine several local feature detection algorithms, which we term weak classifiers into a robust compound classifier using adaptive boosting or AdaBoost. This compound classifier combines the advantages of each individual local classifier. Our primary application area is vortex detection in fluid dynamics datasets. We demonstrate the efficacy of our approach by applying the compound classifier to a variety of fluid dynamics datasets.

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Acknowledgments

This work has been funded by the China National Natural Science Foundation( Grant No. U1035004 and 61003149).

Author information

Authors and Affiliations

  1. Shandong University, Shandong, China

    Li Zhang & Xiangxu Meng

  2. The Ohio State University, Columbus, OH, USA

    Raghu Machiraju

  3. Mississippi State University, Starkville, MS, USA

    David Thompson

  4. University of Florida, Gainesville, FL, USA

    Anand Rangarajan

Authors
  1. Li Zhang
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  2. Raghu Machiraju
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  3. David Thompson
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  4. Anand Rangarajan
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  5. Xiangxu Meng
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Corresponding author

Correspondence to Li Zhang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, People's Republic of China

    Fuchun Sun

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, People's Republic of China

    Tianrui Li

  3. Department of Computer Science and Techn, Tsinghua University, Beijing, People's Republic of China

    Hongbo Li

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© 2014 Springer-Verlag Berlin Heidelberg

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Zhang, L., Machiraju, R., Thompson, D., Rangarajan, A., Meng, X. (2014). Adaptive Boosting for Enhanced Vortex Visualization. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_49

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  • DOI: https://doi.org/10.1007/978-3-642-37829-4_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37828-7

  • Online ISBN: 978-3-642-37829-4

  • eBook Packages: EngineeringEngineering (R0)

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