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A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Zhenhua Guo,
Lei Zhang,
David Zhang
2012 IEEE Transactions on Image Processing
NTNU-CSIE Chen-Lin Yu , Total page:23
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Outline
Introduction Brief Review of LBP Completed LBP (CLBP) Experiments Conclusion
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INTRODUCTION
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Texture Classification
Texture classification is an active research topic in computer vision and pattern recognition.
In texture classification, the goal is to assign an unknown sample image to one of a set of known texture classes.
Texture classification process involves two phases:
(1) learning phase and (2) the recognition phase
Divided texture analysis methods into four categories:
Statistical
Geometrical
Signal processing
Model-based
http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OJALA1/texclas.htm
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Goal:
Proposing a new local feature extractor to generalize and complete LBP(CLBP).
Some information is missed in LBP code. We attempts to address that how to effectively represent the missing information in the LBP so that better texture classification.
In CLBP, a local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT).
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BRIEF REVIEW OF LBP
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LBP
gc is the gray value of the central pixel
gp is the value of its neighbors
P : is the total number of involved neighbors
R : is the radius of the neighborhood
gp: (Rcos(2p / P), Rsin(2p / P))
Example:
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Rotation variance
We define formula U which value of an LBP pattern is defined as the number of spatial transitions (bitwise 0/1 changes) in that pattern.
0 1 1
1 0
0 0 0
1 1 1
1 0
1 0 1
01110000U(LBPP,R) = 2
11110101U(LBPP,R) = 4
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The uniform LBP patterns refer to the patterns which have limited transition or discontinuities (U<=2) in the circular binary presentation [13].
(superscript “riu2” means rotation invariant “uniform” patterns with U<=2)
[13] Multiresolution gray-scale and rotation invariant texture classification with Local Binary Pattern
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COMPLETED LBP (CLBP)
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CLBP Framework
OriginalImage
LocalDifference
LDSMT
Center GrayLevel
S
M
CLBP_S
CLBP_M
CLBP_C
CLBP MapCLBP
HistogramClassifier
(nearest neighborhood)
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I. Local Difference Sign-Magnitude Transform (LDSMT)
a 3*3 sample block local differences
sign components magnitude components
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Fig(b) local differences vector
Fig(c) sign vector Fig(d) magnitude vector
Ex: different vector is [3,9,-13,-16,-15,74,39,31]
after LDMST sign vector is [1,1,-1,-1,-1,1,1,1]
and magnitude vector is [3,9,13,16,15,74,39,31]
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II. CLBP Map
By the LDSMT ,three operators, namely CLBP_C, CLBP_S and CLBP_M, are proposed to code the C, S and M features, respectively
Combine CLBP_S and CLBP_M• 1. Concatenation (CLBP_S_M)• 2. Joint (CLBP_S/M)
Combine three operators• 1. Joint (CLBP_S/M/C)• 2. Hybrid (CLBP_M_S/C or CLBP_S_M/C)
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Dissimilarity Metric and Multi-scale CLBP
There are various metrics to evaluate between two histograms, such as histogram intersection, log-likelihood ratio, and chi-square statistic [13].
The nearest neighborhood classifier with the chi-square distance is used to measure the dissimilarity between two histograms
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EXPERIMENTS
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Outex Database
Includes 24 classes of textures , each texture available at the site is captured using three different simulated illuminants pro-vided in the light source:
– H : 2300K ( 陽光 左下 )
– Inca : 2856K ( 日光燈 右下 )
– TL84 : 4000K( 螢光燈 右上 )
and nine rotation angles
(0o,5º,10º,15º,30º,45º,60º,75º and 90º)
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① CLBP_S better than CLBP_M② CLBP_M/C better than CLBP_M
CLBP_S/M/C better than CLBP_S/M③ LTP better than CLBP_S(more robust to noise)④ CLBP_M than VARP,R
⑤ Finally, CLBP_S/M/C achieves better and more robust results than the state-of-the-art methods LBP VARP,R and VZ_MR8
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CONCLUSION
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We analyzed LBP from a viewpoint of local difference sign-magnitude transform (LDSMT), and consequently a new scheme, namely completed LBP (CLBP)
By fusing CLBP_C 、 CLBP_S 、 CLBP_M codes, it will much better texture classification accuracy than the state-of-the-arts LBP.
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ENDThank you.