Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18950
Title: Is local dominant orientation necessary for the classification of rotation invariant texture?
Authors: Guo, Z
Li, Q
Zhang, L
You, J 
Zhang, D 
Liu, W
Keywords: Image patch
MR8
Rotation invariance
Texton
Texture classification
Issue Date: 2013
Publisher: Elsevier
Source: Neurocomputing, 2013, v. 116, p. 182-191 How to cite?
Journal: Neurocomputing 
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.
URI: http://hdl.handle.net/10397/18950
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2011.11.038
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