Local directional derivative pattern for rotation invariant texture classification

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• 作者：Zhenhua Guo (1)
  Qin Li (2)
  Jane You (3) csyjia@comp.polyu.edu.hk
  David Zhang (3)
  Wenhuang Liu (1)
• 关键词：Texture classification – ; Local binary pattern (LBP) – ; Rotation invariance – ; Local directional derivative pattern (LDDP)
• 刊名：Neural Computing & Applications
• 出版年：2012
• 出版时间：November 2012
• 年：2012
• 卷：21
• 期：8
• 页码：1893-1904
• 全文大小：942.0 KB
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• 作者单位：1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China2. College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China3. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
• ISSN：1433-3058

文摘

Local binary pattern (LBP) is a simple and efficient operator to describe local image pattern. It could be regarded as a binary representation of 1st order derivative between the central and its neighbors. Based on LBP definition, in this paper, a framework of local directional derivative pattern (LDDP) is proposed which could represent high order directional derivative feature, and LBP is a special case of LDDP. Under the proposed framework, like traditional LBP, rotation invariance could be easily defined. As different order derivative information contains complementary features, better recognition accuracy could be achieved by combining different order LDDPs which is validated by two large public texture databases, Outex and CUReT.