Is local dominant orientation necessary for the classification of rotation invariant texture?

Zhenhua Guo, Qin Li, Lin Zhang, Jia You, Dapeng Zhang, Wenhuang Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Extracting local rotation invariant features is a popular method for the classification of rotation invariant texture. To address the issue of local rotation invariance, many algorithms based on anisotropic features were proposed. Usually a dominant orientation is found out first, and then anisotropic feature is extracted by this orientation. To validate whether local dominant orientation is necessary for the classification of rotation invariant texture, in this paper, two isotropic statistical texton based methods are proposed. These two methods are the counterparts of two state-of-the-art anisotropic texton based methods: maximum response 8 (MR8) and gray value image patch. Experimental results on three public databases show that local dominant orientation plays an important role when the training set is less; when training samples are enough, local dominant orientation may not be necessary.
Original languageEnglish
Pages (from-to)182-191
Number of pages10
JournalNeurocomputing
Volume116
DOIs
Publication statusPublished - 20 Sep 2013

Keywords

  • Image patch
  • MR8
  • Rotation invariance
  • Texton
  • Texture classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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