南台科技大學 資訊工程系

Complex Zernike Moments Features for

Shape-Based Image Retrieval

指導教授：李育強報告者 ：楊智雁日期 ： 2010/03/15

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 1, JANUARY 2009

2

Outline

Introduction1

Zernike Moments2

Zm Phase and Magnitude3

Experiments4

5 Conclusion

3

1. Introduction

Existing CBIR systems can be broadly categorized into two groups

Contour and region-based descriptors

As the most commonly used approaches for region-based shape descriptors (geometric moments)

4

1. Introduction (c.)

However, geometric moments do not have any of the desired invariance

Such as translation, scale, or rotation invariance

In this paper, we try to find out the relative importance of the phase and magnitude of Zernike Moments

5

2. Zernike Moments

2/)(

0

2),,()(mn

s

snnm smncR

Basis Function

Radial polynomials

)!2/(()!2/)((!

)!()1(),,(

smnsmns

snsmnc s

)exp()(),( jmRV nmnm

6

2. Zernike Moments (c.)

Zernike moments measurement

2

0

1

0

* ),(),(1

ddVfn

Z nmnm

7

3. Zm Phase and Magnitude

The reconstructed images have far less resemblance to the original image than those by using both magnitude and phase components

2

12 )))(((

))(exp()(),(

n mnmnm

n mnmnmnm

Rc

mjRcI

8

3. Zm Phase and Magnitude (c.)

),(),(),(),( '1,0

' nmRn

Rnm

Rnm m

The corrected phase angle of the rotated image is the same as the corrected phase angle ofthe nonrotated image

),(' Rnm

),(' nm

9

3. Zm Phase and Magnitude (c.)

Angle-based distance and magnitude-based distance

N

iangang id

ND

1

2 )(1

N

imagmag id

ND

1

2 )(1

magmagangang DDD

10

4. Experiments

A. Preparation for Test DBs Scale test DB 、 Rotation test DB 、 Subject test DB 、

Noisy test DB

B. Measurement of Retrieval Performance 1. P−R Graph 2. BEP

11

4. Experiments (c.)

C. Experiment Results 1. IZMD With Different Max Orders

12

4. Experiments (c.)

2. Performance Comparison of IZMD and ZMD

13

4. Experiments (c.)

14

4. Experiments (c.)

3. Performance Comparison of IZMD and GFD

15

4. Experiments (c.)

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5.Conclusion

Rotation 、 translation 、 scaling or change in viewpoint ， We propose here IZMD for robust image retrieval

Its superior performance in noise robustness and subject discriminability when compared with magnitude-only ZMD

We would incorporate object segmentation techniques into the proposed IZMD framework

南台科技大學 資訊工程系