Image Retrieval Using Haar Discrete Wavelet Transform

Advisors: Chi-Hong Lin, Yung-Kuan Chan

Speaker: Chin-Hong Lin (林志鴻 )

Date: 2005-05-04

Outline

• Introduction• Feature of image• Image Retrieval• Experiments• Conclusion

Introduction

LL1 HL1

LH1 HH1

LL2 HL2

LH2 HH2

LL3 HL3

LH3 HH3

LEVEL1LEVEL2LEVEL3

LL:低頻

HL 、 LH:中頻

HH:高頻

Haar Discrete Wavelet Transform

Level 3

Feature of image

N

iixN

Mean1

1

Mean

Std)deviation( Standard

(Sk) Skewness

N

ii Meanx

NStd

1

2)(1

1

N

i

i

Std

Meanx

NSk

1

3)(1

Each Block for R 、 G 、 B Images

N ： total pixels of each block

LEVEL 3

Feature of image

• Image features for R Image

RKRK

RK

RRRRRR SkStdMeanSkStdMeanSkStdMean ,,,...,,,,,, 222111

個90)skewness deviation, standard mean,(3)block(01)BG,R,(3

When LEVEL=3 (K 個 block)

• Image features

個30)skewness deviation, standard mean,(3)block(01

Image Retrieval System

• Distance between query and database images

K

i

rBi

Gi

RiDist

1

1

r

Ri

Ri

Ri

RiRSk

i

r

Ri

Ri

Ri

RiRStd

i

r

Ri

Ri

Ri

RiRMean

iRi

mSkMSk

SkSkW

mStdMStd

StdStdW

mMeanMMean

MeanMeanW

r

Gi

Gi

Gi

GiGSk

i

r

Gi

Gi

Gi

GiGStd

i

r

Gi

Gi

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GiGMean

iGi

mSkMSk

SkSkW

mStdMStd

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mMeanMMean

MeanMeanW

r

Bi

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i

r

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Bi

BiBStd

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r

Bi

Bi

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BiBMean

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mSkMSk

SkSkW

mStdMStd

StdStdW

mMeanMMean

MeanMeanW

Image Retrieval System

• Weight (principal component analysis)

WWS

其中

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k

k

SSS

SSS

SSS

S

21

22221

11211

RMeank

RMean2

RMean1

W

W

W

W

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lMean

RjlMean

Rilij ji

MeanMeann

S1

))((1

1

Experiments

• 系統環境

Database Images: 500張 Query Image: 500張

• Haar Discrete Wavelet Transform LEVEL=6

Experiments (Weight for R)

1 2

5

83 4

6 7

9 10

i

1 0.191913 0.245016 0.236156

2 0.091631 0.194320 0.060509

3 0.120890 0.000036 0.044046

4 0.026260 0.000372 0.064574

5 0.187342 0.100395 0.068993

6 0.015310 0.106159 0.168741

7 0.011028 0.085508 0.002203

8 0.272071 0.002706 0.243292

9 0.000147 0.166620 0.001367

10 0.083409 0.098867 0.110119

RMeaniW

RStdiW

RSkiW

R IMAGE

Experiments (Weight for G)

1 2

5

83 4

6 7

9 10

i

1 0.175647 0.321353 0.391986

2 0.014821 0.104429 0.000194

3 0.231864 0.004607 0.143521

4 0.012855 0.000525 0.002009

5 0.195031 0.150847 0.256818

6 0.005175 0.015622 0.004402

7 0.022285 0.145268 0.027417

8 0.251616 0.001902 0.100477

9 0.002430 0.156640 0.004652

10 0.088277 0.098806 0.068524

GMeaniW

GStdiW

GSkiW

G IMAGE

Experiments (Weight for B)

1 2

5

83 4

6 7

9 10

i

1 0.107564 0.091958 0.421789

2 0.124100 0.317156 0.011024

3 0.168880 0.000112 0.002285

4 0.022146 0.009671 0.132831

5 0.187198 0.190587 0.090834

6 0.007131 0.000868 0.029428

7 0.016765 0.109951 0.005432

8 0.244408 0.000215 0.237243

9 0.007249 0.181187 0.000182

10 0.114560 0.098296 0.068951

BMeaniW

BStdiW

BSkiW

B IMAGE

Experiments

• 查詢結果

ACC 方法 (%) L

HDWT CVAAO CCH FCH

1 94.2 91.8 80.8 83.4

2 96.6 94.8 87.6 87.4

3 97.6 96.8 90.8 89.4

4 97.8 97 92.2 90.2

5 98.2 97.4 93.6 92.2

Experiments

Rotation variant images Shift variant images

Experiments

Texture variant images

Noise variant images

Noise variant images(indecipherable )

Conclusion

• 本論文利用頻帶之間的權重突顯出影像特性達到加強查詢效果。

• 本論文實驗結果能提供不錯的查詢效果，且對影像大小變異、物件位移變異、旋轉變異以及紋理變異，均能有不錯的抵抗能力。

THE END THANK YOU