Human detection method based on non-redundant gradient semantic local binary patterns

Jiu Xu, Ning Jiang, Wenxin Yu, Heming Sun, Satoshi Goto

研究成果: Article査読

2 被引用数 (Scopus)


In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for human detection as a modified version of the conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are performed for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, and to the best of our knowledge, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions of NRGSLBP is necessary, and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).

ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
出版ステータスPublished - 2015 8 1

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学


「Human detection method based on non-redundant gradient semantic local binary patterns」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。