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Shape Based Image Retrieval Using Fourier Descriptors. Dengsheng Zhang and Guojun Lu Gippsland School of Computing and Information Technology Monash University Churchill, Victoria 3842 Australia dengsheng.zhang, [email protected]. Outline. Introduction Shape Signatures - PowerPoint PPT Presentation
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Shape Based Image Retrieval Using Fourier Descriptors
Dengsheng Zhang and Guojun Lu
Gippsland School of Computing and Information Technology
Monash University
Churchill, Victoria 3842
Australia
dengsheng.zhang, [email protected]
Outline
Introduction Shape Signatures Fourier Descriptors Retrieval Experiments Conclusions
Introduction-I--shape feature
What features can we get from a shapeshape?
perimeter, area, eccentricity, circularity, chaincode…
Introduction-II--Classification
Shape
Contour Region
Structural
SyntacticGraphTreeModel-drivenData-driven
PerimeterCompactnessEccentricityFourier DescriptorsWavelet DescriptorsCurvature Scale SpaceShape SignatureChain CodeHausdorff DistanceElastic Matching
Non-Structural AreaEuler NumberEccentricityGeometric MomentsZernike MomentsPseudo-Zernike MmtsLegendre MomentsGrid Method
Introduction-III--criteria
Criteria for shape representation Rotation, scale and translation Invariant Compact & easy to derive Perceptual similarity Robust to shape variations Application Independent
FD satisfies all these criteria Problem
Different shape signatures are used to derive FD, which is the best?
Shape Signatures
Complex Coordinates Central Distance Chordlength Curvature Cumulative Angles Area function Affine FD
Complex Coordinates
z(t) = [x(t) – xc] + i[y(t) - yc]
1
0
1
0
)(1
,)(1 N
tc
N
tc ty
Nytx
Nx
Central Distance
r(t) = ([x(t) – xc]2+ [y(t) - yc]
2)1/2
Chordlength
The chord length function r*(t) is derived from shape boundary without using any reference point
Cumulative Angular Function
(t) = [ (t) - (0)]mod(2)
L is the perimeter of the shape boundary
tLt
t )2
()(
Curvature Function
K(t) = (t) - (t-1)
w is the jumping step in selecting next pixel
)()(
)()(arctan)(
wtxtx
wtytyt
Area Function
|)()()()(|2
1)( 1221 tytxtytxtA
Fourier Descriptors
Fourier transform of the signature s(t)
un, n = 0, 1, …, N-1, are called FD denoted as FDn
Normalised FD
Where m=N/2 for central distance, curvature and angular functionm=N for complex coordinates
1
0
)2
exp()(1 N
tn N
ntjts
Nu
]||
||,...,
||
||,
||
||[
00
2
0
1
FD
FD
FD
FD
FD
FD mf
Affine Invariants
**
**
pppp
pkpkk
XYYX
XYYXQ
k = 1, 2, …
where Xk, Yk are the Fourier coefficients of x(t), y(t) respectively
Convergence Speed-I• Finite number of coefficients are used to approximate the signal. The partial Fourier sum of degree n of u(t) is given by
nk
jktn ekutuS
||
)(ˆ))((
)())((lim tutuSnn
• For piecewise smooth function u(t), there exists a one-to-one correspondence between u(t) and the limit of their Fourier series expansion
• For shape retrieval application, the number of coefficients to represent a shape should not be large, therefore, the convergence speed of the Fourier series derived from the signature function is crucial
Convergence Speed-II
r(t)
(t)
r*(t) z(t)
k(t)
(t)
Convergence Speed-III
• Ten very complex shapes are selected to simulate the worst convergence cases
Signature functions
Number of normalized spectra greater than 0.1
Number of normalized spectra greater than 0.01
r(t) 15 120
r*(t) 40 360
A(t) 20 210
z(t) 10 50
(t) 40 280
(t) k(t) 100 600
Qk 20 100
FD Indexing
Indexing each shape in the database with its Fourier Descriptors
Similarity between a query shape and a target shape in the database is
2/1
1
2 ))((
m
i
ti
qi ffd
lyrespectiveshapestwotheofvectorsfeature
thearefffandfffwhere mtttt
mqqqq ),...,,(),...,,( 2121 ff
Retrieval Experiments
A database consisted of 2700 shapes is created from the contour shape database used in the development of MPEG-7. MPEG-7 contour shape database is consisted of set A, B and C. Set A has 421 shapes, set B has 1400 shapes which are generated from set A through scaling, affine transform and arbitrary deformation and defection. Set C has 1300 shapes, it is a database of marine fishes.
Performance measurement: precision and recall Precision P is the ratio of the number of relevant retrieved shapes r to the total
number of retrieved shapes n. Recall R is the ration of the number of relevant retrieved shapes r to the total number m of relevant shapes in the whole database.
m
rR
n
rP
Results
0
1020
30
4050
60
70
8090
100
0 10 20 30 40 50 60 70 80 90 100 110
Recall
Pre
cisi
on
aff ine FDs
area function FDs
central distance FDs
chord length FDs
curvature function FDs
position function FDs
psi function FDs
Conclusions A comparison has been made between FDs derived
from different shape signatures, FDs with affine FDs In terms of overall performance, FDs derived from
central distance outperforms all the other FDs Curvature and angular function are not suitable for
shape signature to derive FDs due to slow convergence Affine FD is designed for polygon shape, it does not
perform well on generic shape Indexing data structure will be studied in the future
research Comparison with other shape descriptors