LBP & More - PowerPoint Presentation

Slide1

LBP & More

Slides

from Dr.

Shahera

HossainSlide2

Pixel Neighborhood-based Feature

The most important for texture analysis is to

describe

the spatial behavior of intensity values in any given neighborhood.

Different

methodologies have been proposed.

Local binary pattern (LBP) is one of the most-widely used approach – mainly for face recognition.

LBP is used for texture analysis too. Slide3

Local Binary Pattern (LBP)

Binary

threshold function

is,

For each PIXEL of an image, a BINARY CODE is produced

 to make a new matrix with the new value (binary to decimal value).

where

, neighborhood pixels in each block is thresholdedby its center pixel value p sampling points (e.g., p = 0, 1, …, 7 for a 3x3 cell, where P = 8)r  radius (for 3x3 cell, it is 1).

 Slide4

Component-wise multiplication

Computation of Local Binary Pattern

1

2

4

128

8

64

32

16020128

?

0

0

0

16

0101Nc0001

3

7

2

8

4

1

235

∑

146

Neighborhood

of a gray-scale image

Binary code for >

N

c

Representation

Sum

LBP

Example of how the LBP operator works

topSlide5

Computation of LBP

Code/Weight

:

1 x 2­

7

1 x 2­

6

1 x 2­5

1 x 2­40 x 2­3

0x 2­20x 2­11 x 2­0 = 128= 64= 32= 16= 0= 0= 0= 1

1 x 2­

7

1 x 2­

6

1 x 2­51 x 2­40 x 2­30x 2­2

0x 2­11 x 2­0 = 128= 64= 32= 16= 0= 0= 0= 10

(LSB

)

Binary Pattern:

1

(MSB)

1

110001(LSB)LBP:1 + 0 + 0 + 0 + 16 + 32 + 64 + 128 = 241Slide6

A New Method

: DCLBP

A

new method called

diagonal-crisscross local binary pattern (DCLBP) for texture

representation is proposed recently.Basic concept: An image feature should take diagonal pixel variations as well as horizontal and vertical (crisscross) pixel variations in the neighborhood, so that it can perform well even in the cases of rotations in images.

Two

other variants of the LBP, considering rotational feature & mean/median of the neighbor pixels are also proposed

These are compared with LBP, median-LBP, interpolation-LBP, number-LBP, neighborhood-intensity-LBP.Slide7

Diagonal-Crisscross

Local Binary Pattern

(DCLBP

)

(a)

(b)

(c)(d)

(LSB)

N

0 – N4N1 – N5N2 – N6N3 – N7topSlide8

3x3

image

patch

Start

from N

0 pixel

positionGet differences for front-diagonal values (N0 – N4), in vertical direction (N1 – N

5), for back-diagonal

direction (N2 – N6), and in horizontal direction (N

3 – N7)Multiply each difference with 21, 23, 25 and 27 sequentially to compute a new valueTake the mean value of newly-computed value and the central pixel value Diagonal-Crisscross Local Binary Pattern (DCLBP)…1357Slide9

where

,

Sampling-point

set is,

Cardinality

of the set is

,

Difference

parameter

is,

The binary threshold function

is,

Diagonal-Crisscross Local Binary Pattern…

p

0 = 21 p1 = 23 p2 = 25 p3 = 27 Slide10

Here, the cardinality,

that demonstrates that it has 4 different-possible values as ‘2

1

, 2

3

, 25 and 27’. when

, we get = which, covers the front-

diagonal difference of

. Similarly,when

covers the vertical difference of ;when covers the back-diagonal difference of ;when covers the horizontal difference of Through

this manner, we get the new central pixel value for each patch.

Diagonal-Crisscross Local Binary Pattern…Slide11

Median-RILBP

where

,

symbolizes ‘rotational-invariant’ nature;

is pattern (e.g., 8 for a 3x3 patch);

is

radius (1 for 3x3 patch);

Rho (

)

is the rotational function where it circularly does bitwise right-shift operation. is 8 when if . so 8 times rotationThe bit-shift is done 8 times if . The concept here is to rotate the neighbors.Finally, compute the value that the neighbor chain may represent. Slide12

Mean-RILBP

where

,

symbolizes ‘rotational-invariant’ nature;

is pattern (e.g., 8 for a 3x3 patch);

is radius (1 for 3x3 patch);

Rho (

) is the rotational function where it circularly does bitwise right-shift operation.

is 8 when if

. so 8 times rotationThe bit-shift is done 8 times if . The concept here is to rotate the neighbors.Finally, compute the MEAN value. Slide13

Experiment: Dataset 1

Figure:

Sample images for each class of USC-SIPI Rotated Textures dataset.

Figure:

Seven rotated images for

bark

class (USC-SIPI Rotated Textures dataset).Slide14

USC-SIPI

(i.e., University of Southern California – Signal and Image Processing Institute))

Rotated

Textures

dataset

Each image is digitized at seven different rotation angles: 0, 30, 60, 90, 120, 150, and 200 degree

for each of the 13 different imagesThese images are taken from the Brodatz database which is the most widely-used dataset

Experiment: Dataset 1 – USC-SIPI DB

USC-SIPI DatabaseTotal classes13

No. of images per class7Rotations7Image size512x512 pixelsTotal images91Slide15

Experiment: Dataset

2

Ten different classes for KTH-TIPS dataset

(sandpaper

, crumpled aluminum foil,

styrofoam

, sponge, corduroy, linen, cotton, brown bread, orange peel and cracker B) Slide16

Experiment: Dataset

2 – KTH-TIPS Database

KTH-TIPS

(TIPS stands for ‘Textures under varying Illumination, Pose and Scale’)

database.

KTH-TIPS Database

Total classes10No. of images per class81Rotations & variations81Image size200x200 pixelsTotal images

810Slide17

Classification

Lets

employ

K-nearest neighbor classifier (KNN) &

Multi-class support vector machine (SVM) (radial basis kernel function, gamma = 1). We consider 10-fold cross-validationSlide18

Recognition Results – 1

USC-SIPI

R

otated Textures

DB

Feature

KNN SVM

LBP

46.074.7

Interpolation_LBP46.180.2Median_LBP30.880.2MedianR_LBP84.693.4Number_LBP92.387.9NI_LBP23.165.9

Mean-RILBP

100

95.6

Median-RILBP

92.396.7DCLBP10090.1

USC-SIPI DatabaseTotal classes13No. of images per class7Rotations7Image size512x512Total images91Slide19

Recognition

Results – 2

KTH-TIPS DB

Feature

KNN

SVM

LBP

8053.7Interpolation_LBP

6053.1Median_LBP7046.4MedianR_LBP7028Number_LBP9035.2NI_LBP7055.7Mean-RILBP

70

23.4

Median-RILBP

70

29.6DCLBP9072.5

KTH-TIPS DatabaseTotal classes10No. of images per class81Rotations & variations81Image size200x200Total images810topSlide20

Thank you very much for your kind attention