JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 32 Issue 3
May 2017
Article Contents
GUO Yan-hui, YIN Xi-jie and ZHANG Hong. An improved local binary algorithm for image categorization[J]. Journal of Light Industry, 2017, 32(3): 73-77. doi: 10.3969/j.issn.2096-1553.2017.3.012
Citation: GUO Yan-hui, YIN Xi-jie and ZHANG Hong. An improved local binary algorithm for image categorization[J]. Journal of Light Industry, 2017, 32(3): 73-77. doi: 10.3969/j.issn.2096-1553.2017.3.012 shu

An improved local binary algorithm for image categorization

  • Received Date: 2016-11-01
    Available Online: 2017-05-15
  • In the extraction process of LBP features,most consumption of time and memory were paid for clustering.In order to address these problems,an improved local binary algorithm for image categorization was proposed.The algorithm replaced decimal system encoding LBP with binary descriptor.Meanwhile,Hamming distance was emploied rather than Euclidean metric for features clustering.The multi-scale LBP features was flued for a new local binary descriptor.The result of the experiment on the PASCAL VOC 2007 dataset showed that the adopted local binary descriptor was better than the classical LBP,specifically for time consumption.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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