DocumentCode :
643788
Title :
Dominant multi-dimensional local binary patterns
Author :
Doshi, Niraj P. ; Schaefer, Gerald
Author_Institution :
Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK
fYear :
2013
fDate :
5-8 Aug. 2013
Firstpage :
1
Lastpage :
6
Abstract :
Local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture characterisation. Multi-dimensional LBP (MD-LBP) utilises this concept, while also maintaining the relationships between the different scales by building a multi-dimensional histogram of LBP features. Although this has been shown to give good discriminatory power, the resulting feature vectors are also rather large. In this paper, we show that Dominant MD-LBP (D-MD-LBP), which utilises only dominant texture bins, provides an effective texture descriptor of reduced dimensionality as our experimental results, run on three benchmark datasets of the Outex test suite, confirm.
Keywords :
feature extraction; image coding; image texture; LBP descriptor; LBP feature; dominant MD-LBP; local neighbourhood property; multidimensional histogram; multidimensional local binary patterns; multiresolution texture; texture descriptor encoding; Accuracy; Benchmark testing; Databases; Feature extraction; Histograms; Training; Vectors; local binary patterns; multi-dimensional LBP; texture; texture classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on
Conference_Location :
KunMing
Type :
conf
DOI :
10.1109/ICSPCC.2013.6664108
Filename :
6664108
Link To Document :
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