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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2012

01.11.2012 | ICIC2010

Local directional derivative pattern for rotation invariant texture classification

verfasst von: Zhenhua Guo, Qin Li, Jane You, David Zhang, Wenhuang Liu

Erschienen in: Neural Computing and Applications | Ausgabe 8/2012

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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.
Vorheriger Artikel Construction of gene regulatory networks with colored noise
Nächster Artikel Dynamic transition embedding for image feature extraction and recognition

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Metadaten
Titel
Local directional derivative pattern for rotation invariant texture classification
verfasst von
Zhenhua Guo
Qin Li
Jane You
David Zhang
Wenhuang Liu
Publikationsdatum
01.11.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 8/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0586-6

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