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Description of Rotation-Invariant Textures using Local Binary Pattern Features

by Prashant H. Gutte, Prashant K. Kharat
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 9
Year of Publication: 2014
Authors: Prashant H. Gutte, Prashant K. Kharat
10.5120/17404-7969

Prashant H. Gutte, Prashant K. Kharat . Description of Rotation-Invariant Textures using Local Binary Pattern Features. International Journal of Computer Applications. 99, 9 ( August 2014), 28-31. DOI=10.5120/17404-7969

@article{ 10.5120/17404-7969,
author = { Prashant H. Gutte, Prashant K. Kharat },
title = { Description of Rotation-Invariant Textures using Local Binary Pattern Features },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 9 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number9/17404-7969/ },
doi = { 10.5120/17404-7969 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:47.520812+05:30
%A Prashant H. Gutte
%A Prashant K. Kharat
%T Description of Rotation-Invariant Textures using Local Binary Pattern Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 9
%P 28-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture classification is one of the most interesting research topics in the field of computer vision. This paper aims at classifying static as well as dynamic textures (DT). Uniform Local binary pattern (LBP) is a combination of structural and statistical analysis model for classification of both static and dynamic textures. The LBP Histogram Fourier (LBP-HF) uses Fast Fourier Transform (FFT) for calculating global rotation invariant of LBP histogram for static texture classification. For dynamic texture classification, LBP-TOP method is used, which computes LBP from three orthogonal planes. It combines both motion and appearance together for classification. The results show that, LBP-HF outperforms uniform LBP as well as basic rotation invariant LBP method for static-textures and LBP-TOP gives higher accuracy for dynamic-textures.

References
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  7. H. Arof and F. Deravi, "Circular neighbourhood and 1-D DFT features for texture classification and segmentation," Proc. Inst. Elect. Eng. —Vis. , Image Signal Process. , vol. 145, no. 3, pp. 167–172, Jun. 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Texture Local Binary Pattern LBP-TOP FFT

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