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Extremely Fast Unsupervised Codebook Learning for Landmark Recognition

  • Conference paper
Modern Advances in Applied Intelligence (IEA/AIE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8481))

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Abstract

Traditional landmark recognition methods work by using local image features, k-means vector quantization and classifiers like SVM to recognize landmarks. However, the inefficient codebook learning by k-means constraints the possibility of using high-dimensional feature spaces, large numbers of image descriptors and large codebooks which are needed for good results. In this paper we introduce a fast unsupervised codebook learning - Extremely Random Projection Forest (ERPF), which is an ensemble of random projection tree with randomly splitting direction. We evaluate our approach on two public datasets and ERPF significantly outperforms other spatial tree methods and k-means.

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Authors and Affiliations

  1. HTC Beijing Advanced Technology and Research Center, Beijing, P.R. China

    Yilin Guo & Wanming Lu

Authors
  1. Yilin Guo
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  2. Wanming Lu
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, TX 78666, San Marcos, USA

    Moonis Ali

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (ROC)

    Mong-Fong Horng

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© 2014 Springer International Publishing Switzerland

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Guo, Y., Lu, W. (2014). Extremely Fast Unsupervised Codebook Learning for Landmark Recognition. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-07455-9_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07454-2

  • Online ISBN: 978-3-319-07455-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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