Robust texture image representation by scale selective ...

郭振华清华大学深圳研究生院

zhenhua.guo@sz.tsinghua.edu.cn

Robust texture image

representation by scale

selective local binary patterns

(TIP2016)

清华大学深圳研究生院

清华大学深圳研究生院成立于2001年。为清华大学唯一的异地办学机构。同一学校，同一品牌，秉承同一文化传统。

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3

清华大学深圳研究生院

Outline

Texture definition and challenge

Texton (Statistical vs. Binary)

Overview of LBP and CLBP

Proposed SSLBP

Experimental Results and Discussion

4

Texture Definition

Definition

Wiki Dictionary:

The feel or shape of a surface or substance;

the smoothness, roughness, softness, etc. of

something.

In fact, the definition of texture is still an open

issue.

Texture is everywhere

6

草地(纹理)

树林(纹理)

楼房(纹理)

天空(纹理)

Wide Application

7

Desert vs Mountain

Normal vs Abnormal

Defect Detection

Common issues

Lightness, rotation and scale

8

9

Structural approach: a set of texels in some regular or repeated pattern

Structural Approach

10

How do you decide what is a texel?

Limitation of Structure

Approach

grass leaves

What/Where are the texels?

Statistical Texton

11

Binary Texton

12

Two advantages:

Fast

Insensitive to training set

LBP in spatial domain

13T. Ojala, M. Pietikäinen, and T. T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification

with Local Binary Pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), pp. 971-987, 2002.

LBP and contrast operators

14

Circle-LBP

15

Multiscale LBP

16

An example of LBP image

and histogram

17

Rotation Invariance (ri)

18

Rotation Invariance

19

Completed LBP

Central pixel and its P circularly and evenly spaced neighbours with radius R.

(a) (b) (c) (d)

(a) A 33 sample block; (b) the local differences; (c) the sign and (d) magnitude components.

24

Completed LBP

21

OriginalImage

LDSMT

CLBP Map

Center Gray Level CLBP_C

CLBP_SS

CLBP_MM

Local Difference

CLBPHistogram

Classifier

Representation Example

Histogram of CLBP_S of a sample. Histogram of CLBP_M of the sample.

Histogram of CLBP_S_M. Histogram of CLBP_S/M.Zhenhua Guo, Lei Zhang, David Zhang, A Completed Modeling of Local Binary Pattern Operator for Texture

Classification, IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1657-1663, 2010.

Scale Variation

Scale invariance is more difficult.

Two popular ways:

Local scale invariance

Global scale invariance

23

Scale Invariance (I)

24

Local scale invariance

Detect a Harris or Laplacian Region->Normalize the

region->Feature Extraction

Scale Invariance (I)

25

Local scale invariance

Estimating local scale or extracting local fractal

feature

Scale Invariance (II)

26

Global scale invariance

Global fractal feature

Scale Invariance (II)

27

Global scale invariance

Polar transform

Scale Invariance (II)

28

Global scale invariance

Scale shift matching

Assumption

29

Statistical dominant local patterns provide

discriminant information for texture classification.

When an image changes scale, percentage of

dominant patterns does not change.

S. Liao, M. W. K. Law, and A. C. S. Chung. Dominant local binary patterns for texture classification. IEEE

Transactions on Image Processing 18(5), pp. 1107-1118, 2009.

Algorithm 1:

Feature Learning

Step 1: for one training sample, build a scale space by a 2D

Gaussian filter;

Step 2: compute local pattern histogram for each image;

Step 3: only maximal frequency among different scale is

kept;

Step 4: compute average frequency for the whole training

set;

Step 5: dominant patterns with high frequency are learnt.

30

1

, =1

, 1< L, "*" is the convolution operator

i

l

l

f ls

s g l

1 2( )=max( ( ), ( ), ..., ( ))i Lf s s s

CLBP _ S / C CLBP _ S / C CLBP _ S / C CLBP _ S / CH k H k H k H k

( )( )= ( )+

if

CLBP _ S / CT T

CLBP _ S / C CLBP _ S / C

H kH k H k

N

Algorithm 2:

Feature Extraction

Step 1: for one test sample, build a scale space by a 2D

Gaussian filter;

Step 2: compute histogram for selected patterns by algorithm

1;

Step 3: only maximal frequency among different scale is kept.

31

1

, =1

, 1< L, "*" is the convolution operatorl

l

I ls

s g l

1 2( )=max( ( ), ( ), ..., ( ))Ls s sI

CLBP _ S / C CLBP _ S / C CLBP _ S / C CLBP _ S / CDPH k DPH k DPH k DPH k

Scale selective LBP

(SSLBP)

32

.

.

.

1 : Original ImageIs

Convolved by 2D

Gaussian function g

Image

Scale

Space

2

Is

I

Ls

0 05. 0 1. 0 08. 0 03. 0 02.

Feature Size: K

Feature frequency of riP ,R

T

CLBP _ M / CDP

Feature frequency

of riP ,R

T

CLBP _ S / CDP

Feature Size: K

0 14. 0 01. 0 02. 0 05. 0 0.

0 06. 0 07. 0 06. 0 05. 0 0.

Feature frequency of riP ,R

T

CLBP _ M / CDP

Feature frequency

of riP ,R

T

CLBP _ S / CDP

0 15. 0 01. 0 02. 0 01. 0 04.

Feature frequency of riP ,R

T

CLBP _ M / CDP

Feature frequency

of riP ,R

T

CLBP _ S / CDP

Feature

Scale

Space

0 15. 0 1. 0 08. 0 05. 0 04.

operationMax

0 14. 0 02. 0 07. 0 05. 0 02.

0 09. 0 02. 0 04. 0 03. 0 02.

0 13. 0 01. 0 07. 0 01. 0 01.

Output feature

for image I

Feature Size: 2K

operationMax

An example

33

5 10 15 20 25 30 35 40 45 500

0.01

0.02

0.03

0.04

0.05

0.06

Pattern Index

Fre

quency(%

)

5 10 15 20 25 30 35 40 45 500

0.01

0.02

0.03

0.04

0.05

0.06

Pattern Index

Fre

quency(%

)

5 10 15 20 25 30 35 40 45 500

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Pattern Index

Fre

quency(%

)

5 10 15 20 25 30 35 40 45 500

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Pattern Index

Fre

quency(%

)

0.35

0.17

5 Dominant Patterns

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Scale Estimation

351 2 3 4 5 6 7 8 91.7

1.75

1.8

1.85

1.9

1.95

2

2.05

2.1

Image Scale

Avera

ge F

eatu

re S

cale

Scale parameter of KTH-TIPS.

, 1, 2,...,990

z

I

I IS

z

FS

AFS z

K

kScaleIndex

FS

K

k

I

I

1

)(

Texture Databases

36

Texture

Database

Name

Imaging property Image Size

Number

of

classes

Number

of

samples

CUReTThe images are captured under different illumination and

viewing directions. Fixed, 200*200 61 92

KTH-

TIPS

It extends CUReT by imaging new samples of ten of the

CUReT textures at a subset of the viewing and lighting

angles used in CUReT but also over a range of scales.

Varied,

196*201 10 81

UIUC

Textures are acquired under significant scale and viewpoint

changes, arbitrary rotations, and uncontrolled illumination

conditions, even including textures with non-rigid

deformation.

Fixed, 640*480 25 40

UMDIt has been designed in a similar way as UIUC, while the

image resolution is 4 times of UIUC.

Fixed, 1280*

96025 40

A LOTIt is systematically collected with varied viewing angles,

illumination angles, and illumination colors for each

material.

Varied, 1536*

891250 100

CUReT Database

37

KTH-TIPS Database

38

39

UIUC Database

40

UMD Database

ALOT Database

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Parameters

Scale Space: 4

2D Gaussian filter: 20.25

Radius: 3, 9

Neighbor: 24

Feature extractor: CLBP_S/C, CLBP_M/C

Feature Length: 2400

NNC: NNC+Chi-square distance

Feature preprocessing: 42

( ), 1,2,...,k kH sqrt H k K

Nearest Subspace

Classifier (NSC)

There are C classes of textures.

n training samples in each class, a set of

histograms for one class:

project hy into the subspace spanned by Hc :

The projection residuals is computed as:

43

1( )T T yc c c cH H H h

2

yc c cerr h H

,1 ,2 ,, ,...,c c c c nH h h h

K. Lee, J. Ho, and D. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE

Transactions on Pattern Analysis and Machine Intelligence 27(5), pp. 684–698, 2005.

Experimental ResultsMethod CUReT

(46)

KTH-TIPS

(40)

UIUC

(20)

UMD

(20)

ALOT

(50)

SRP [ICCV2011] (SVM) 99.37 99.29 98.56 99.30 -

RP [TPAMI2012] (NNC) 98.52 97.71 96.27 99.13 -

Caputo et al. [IVC2010] (SVM) 98.46 94.8 92.0 - -

BIF [IJCV2010] (Shift Matching NNC) 98.6 98.5 98.8 - -

OTF [CVIU2010] (SVM) - - 97.44 98.42 95.6

WMFS [TIP2013] (SVM) - - 97.62 98.68 96.94

PLS [CVPR2014](SVM) - 98.4 96.57 98.99 93.35

PFS [IVC2014](SVM) - 97.35 97.92 99.38 97.5

LEP [TIP2013] (Shift Matching NNC) - 97.56 - - -

scLBP [TIP2015] (SVM) 99.29 - 98.45 99.25 -

COV-LBPD [TIP2014] (NNC) - 98.0 - - -

RPICoLBP [TPAMI2014] (SVM) 98.4 98.4 - - -

RLBP [BMVC2013] (NNC) - - 96.7 - -

DLBP [TIP2009] (NNC) 84.93 86.99 60.73 89.87 78.38

LBPSRI [TIP2012] (NNC) 85.00 89.73 70.05 91.71 71.29

LBP [TPAMI2002] (NNC) 80.63 82.67 55.26 88.23 63.33

CLBP [TIP2010] (NNC) 97.40 97.19 93.26 98.00 93.28

SSLBP (NNC) 98.55 97.80 97.02 98.84 96.69

SSLBP (NSC) 99.51 99.39 99.36 99.46 99.7144

Time Cost (I)

Method

Scale Space

Building

Pattern/

Patch

Processing

HistogrammingClassification

(NNC)

RP [TPAMI2012] - SP·SR·Ip SR·C·ST·Ip C·ST·(C·Tn)

VZ_Patch

[TPAMI2009]- - SP·C·ST·Ip C·ST·(C·Tn)

SSLBP (L-1)·Sg· Ip 2·L·P·Ip 2·L·Ip 4·K·(C·Tn)

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Here L=4 is the size of scale space, P=24 is the number of neighbours for LBP, Ip denotes the number of pixels

per image sample, Sg denotes the size of Gaussian smooth kernel, K=600 is the number of selected dominant

patterns. SP represents the size of a local patch, usually 7*7, SR is the dimension of random projection, usually

15, C denotes the number of classes, ST represents the number of clustered textons per class, here C·ST≈4·K. Tn

is the number of training samples per class.

CUReT KTH-TIPS UIUC UMD ALOT

Feature extraction of MFS

(ICCV2009) (Unit: Second)0.09 0.08 0.62 2.60 2.67

Feature extraction of VZ_MR8

(ICCV2005) (Unit: Second)1.03 0.93 9.98 37.35 40.11

Feature extraction of VZ_Patch

(TPAMI2009) (Unit: Second)12.44 11.28 96.97 309.51 346.64

Feature extraction of the

proposed scheme

(Unit: Second)

0.24 0.23 1.80 7.63 8.46

Matching (NNC)

(Unit: Millisecond)177.47 25.09 30.78 31.1 297.42

Matching (NSC)

(Unit: Millisecond)2.25 0.64 0.90 0.92 5.05

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77

Time Cost (II)

Robust to image size

Image size

\#Training

Sample

20 15 10 5

1280*960 99.46+0.46 99.31+0.51 98.81+0.70 96.41+1.28

640*480 99.71+0.26 99.48+0.37 98.77+0.68 95.68+1.45

320*240 99.38+0.45 98.80+0.69 97.41+1.06 92.74+1.84

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Test on UMD

Dominant patterns

analysis

CUReT KTH-TIPS UMD UIUC

CUReT 100% 80.33% 73% 74.54%

KTH-TIPS - 100% 76.12% 80.16%

UMD - - 100% 87.87%

UIUC - - - 100%

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Percentage of identical dominant patterns between different training sets.

Robust to pattern selection

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Discussion

Traditional methods try to extract local or

global scale invariant features.

From implementation view, extract local

scale variant feature first, then apply a

global transformation to achieve invariance.

From scale space view, instead of analyzing

scale spaces locally, analyze scale space

globally.

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Conclusion

A simple and effective method to address

scale variation issue for texture image.

Fast enough for many applications, 0.24

second for a 200*200 image.

LBP with scale selection can get promising

result for challenge databases, such as UIUC

and ALOT.

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谢谢！For any inquiry, please contact with zhenhua.guo@sz.tsinghua.edu.cn