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Local Hypersphere Coding Based on Edges between Visual Words

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7724)

Abstract

Local feature coding has drawn much attention in recent years. Many excellent coding algorithms have been proposed to improve the bag-of-words model. This paper proposes a new local feature coding method called local hypersphere coding (LHC) which possesses two distinctive differences from traditional coding methods. Firstly, we describe local features by the edges between visual words. Secondly, the reconstruction center is moved from the origin to the nearest visual word, thus feature coding is performed on the hypersphere of feature space. We evaluate our coding method on several benchmark datasets for image classification. The experimental results of the proposed method outperform several state-of-the-art coding methods, indicating the effectiveness of our method.

Keywords

  • Local Feature
  • Visual Word
  • Benchmark Dataset
  • Sparse Code
  • Code Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Ren, W., Huang, Y., Zhao, X., Huang, K., Tan, T. (2013). Local Hypersphere Coding Based on Edges between Visual Words. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-37331-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37330-5

  • Online ISBN: 978-3-642-37331-2

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