Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8033
Title: Local directional derivative pattern for rotation invariant texture classification
Authors: Guo, Z
Li, Q
You, J 
Zhang, D 
Liu, W
Issue Date: 2012
Source: Neural computing and applications, 2012, v. 21, no. 8, p. 1893-1904
Abstract: 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.
Keywords: Local binary pattern (LBP)
Local directional derivative pattern (LDDP)
Rotation invariance
Texture classification
Journal: Neural Computing and Applications 
ISSN: 0941-0643
DOI: 10.1007/s00521-011-0586-6
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