Multi-resolution local binary patterns for image classification

This paper presents a novel method to extract image features for image classification. The extracted feature named multi-resolution local binary pattern (MR-LBP) is based on the local binary pattern (LBP) feature. The MR-LBP feature is highly distinctive by making use of multi-resolution patterns to...

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Bibliographic Details
Published in2010 International Conference on Wavelet Analysis and Pattern Recognition pp. 164 - 169
Main Authors Peng Liang, Shao-Fa Li, Jiang-Wei Qin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Feature extraction
Histograms
Image classification
Image resolution
Local binary pattern
Pixel
Region description
SIFT
Training
Wavelet analysis
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ISBN1424465303
9781424465309
ISSN2158-5695
DOI10.1109/ICWAPR.2010.5576318

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Summary:This paper presents a novel method to extract image features for image classification. The extracted feature named multi-resolution local binary pattern (MR-LBP) is based on the local binary pattern (LBP) feature. The MR-LBP feature is highly distinctive by making use of multi-resolution patterns to obtain more descriptive information. The experiments results demonstrate the proposed MR-LBP feature is robust to image rotation, illumination changes and image noises. We also describe a descriptor called MR-LBP descriptor to using the features for image classification. Through experiments, our proposed approach performs favorably compared with the most well-known SIFT descriptor in two benchmark dataset. What's more, the proposed descriptor is computation simpler than the SIFT descriptor.
ISBN:1424465303
9781424465309
ISSN:2158-5695
DOI:10.1109/ICWAPR.2010.5576318