bayesian joint estimation of the multifractality parameter...
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BAYESIAN JOINT ESTIMATION OF THE MULTIFRACTALITY PARAMETER OFIMAGE PATCHES USING GAMMA MARKOV RANDOM FIELD PRIORS
S. Combrexelle1, H. Wendt1, Y. Altmann2, J.-Y. Tourneret1, S. McLaughlin2 and P. Abry31 IRIT, CNRS, Toulouse Univ., France [email protected]
2 School Engineering Physical Sci., Heriot-Watt Univ., Edinburgh, UK, [email protected] Physics Dept., CNRS, ENS Lyon, France [email protected]
SummaryMultifractal (MF) analysis enables homogeneous image texture to be modeled and studied via the regularity fluctuations of
image amplitudes. In this work, we propose a Bayesian approach for the local (patch-wise) MF analysis of images with
heterogeneous MF properties. To this end, a joint Bayesian model for image patches is formulated using spatially smoothinggamma Markov Random Field priors, yielding a fast algorithm and significantly improving the state-of-the-art performance.
Numerical simulations based on synthetic MF images illustrate the benefits of the proposed MF analysis procedure for images.
Multifractal analysis-Holder exponent:
Roughness of image X around t0�! comparison to a local power law behavior
�! ||X(t)�X(t0)|| C||t� t0||↵ C > 0, ↵ > 0
�! largest such ↵: Holder exponent h(t0)
X(t0)
⇢rough h(t0) ⇠ 0
smooth h(t0) ⇠ 1
-Multifractal spectrum:
D(h) = dimHausdor↵{t : h(t) = h}�! geometric structure of iso-Holder sets of points ti
ti
h(ti) = 0.2
0.2
D(h)
h0
d
-Wavelet-leaders:
Local supremum of wavelet coe�cients
d(m)X (j,k) – 2D DWT coe�cients (m = 1, 2, 3)
9�j,k – dyadic cube centred at k2j and its 8 neighbors
l(j,k) = supm2(1,2,3),�0⇢9�j,k
|d(m)X (�0)|
-Multifractal formalism:
-D(h) 1� (h�c1)2/(2|c2|)+ · · ·
Cp(j) = c0p + cp ln 2j
x p-th cumulant of ln l(j,k)- c2 – multifractality parameter c1
c2
D(h)
h0
d
C2(j) = Var [ln l(j,k)] = c02 + c2 ln 2j
�! classical estimation by linear regression (LF)
Statistical model for log-leaders-Time-domain statistical model: [Combrexelle15]
Centered log-leaders `j = [log l(j, ·)] ⇠ Gaussian random field
– radial symmetric covariance model %j(�k;✓)– parametrized by ✓ = [✓1, ✓2]
T = [c2, c02]T
– assuming independence between scales:
p(`|✓) =j2Y
j=j1
p(`j|✓)/j2Y
j=j1
|⌃(✓)|�12 e
⇣�12`
Tj ⌃(✓)�1`j
⌘
, ` = [`Tj1, ..., `Tj2]T
⌃(✓) - covariance matrix induced by %j(�k;✓)
– Whittle approximation of p(`j|✓)
p(`j|✓) / exp⇣�X
m
log �j(m;✓) +y⇤j(m)yj(m)
�j(m;✓)
⌘
yj = DFT (`j) - Fourier transform of centered log-leaders
�j(m;✓) - spectral density associated with %j(�k;✓)
-Data augmented model in the Fourier domain:
1 [Combrexelle16]– statistical interpretation of Whittle approximation
�! �! y = [yTj1, ...,yT
j2]T complex Gaussian random variable
– reparametrization v = (✓) 2 R+2?
y ⇠ CN (v1F + v2G)
independent positivity contraints on viF and G - diagonal, positive-definite, known and fixed
– data augmentation of the model(y|µ, v2 ⇠ CN (µ, v2G) observed data
µ|v1 ⇠ CN (0, v1F ) hidden mean
�! associated with augmented likelihood
p(y,µ|✓) / v2�NY exp
⇣� 1
v2(y � µ)HG
�1(y � µ)
⌘
⇥ v1�NY exp
⇣� 1
v1µHF
�1µ⌘
pinverse-gamma IG priors on vi are conjugate
[Robert05] Monte Carlo Statistical Methods, Springer, New York, USA, 2005
[Dikmen10] Gamma Markov Random Fields for Audio Source Modeling, IEEE T. Audio,
Speech, and Language Proces., vol. 18, no. 3, pp. 589-601, March 2010
[Combrexelle15] Bayesian estimation of the multifractality parameter for image texture using
a Whittle approximation, IEEE T. Image Proces., vol. 24, no. 8, pp. 2540-2551, Aug. 2015
[Combrexelle16] A Bayesian framework for the multifractal analysis of images using data
augmentation and a Whittle approximation, Proc. ICASSP, Shanghai, China, March 2016
ICIP 2016 – Phoenix, Arizona, USA — September 25-28, 2016
.Bayesian model for image patches- Likelihood:
-{Xk=(kx,ky)} - partition of imageX into non-overlapping patches
-Y ={yk} - collection of Fourier coe�cients-M ={µk} - collection of latent variables
-V i={vi,k} - collection of MF parameters
+ assuming indepence between patches
�! p(Y ,M |V ) /Y
k
p(yk,µk|vk), V = {V 1,V 2}
- Gamma Markov random field (GMRF) prior:
- assuming smooth spatial evolution of MF parameters
- positive auxiliary variables Z = {Z1,Z2}, Z i = {zi,k}, i = 1, 2
�! induce positive correlation between neighbouring vi,k
- each vi,k is connected to 4 variables zi,k0
k0 2 Vv(k) = {(kx, ky) + (ix, iy)}ix,iy=0,1via edges with weights ai
- each zi,k is connected to 4 variables vi,k0
k0 2 Vz(k) = {(kx, ky) + (ix, iy)}ix,iy=�1,0
kx
ky
vi,k
zi,k
′
−→−→
−→−→
- associated distribution [Dikmen10]
p(V i,Z i|ai)=1
C(ai)
Yke�(4ai+1) log vi,k e(4ai�1) log zi,k ⇥e
� aivi,k
Pk02Vv(k) zi,k0
pconditionals for vi,k (zi,k) are inverse-gamma IG (gamma G)
- Posterior distribution & Bayesian estimator:
- Posterior distribution via Bayes’ theorem
p(V ,Z,M |Y , ai) / p(Y |V 2,M ) p(M |V 1)| {z }augmented likelihood
⇥Y
ip(V i,Z i|ai)| {z }
independent GMRF priors
-Marginal posterior mean (MMSE) estimator
V MMSEi = E[V i|Y , ai]| {z }
untractable
⇡ 1
Nmc �Nbi
XNmc
q=Nbi
V (q)i
with {V (q)i }Nmc
q=1 distributed according to p(V ,Z,M |Y , ai)
�! computation via Markov chain Monte Carlo algorithm
- Gibbs sampler: [Robert05]
�! iterative sampling according to conditional distributions
µk |Y ,V⇠CN⇣v1,kF��1
vkyk,
⇣(v1,kF )�1+(v2,kG)�1
⌘�1⌘
v1,k |M ,Z1⇠IG⇣NY+4ai,µ
Hk F
�1µk+�1,k
⌘
v2,k |Y ,M ,Z2⇠IG⇣NY+4ai, (yk � µk)
HG�1(yk � µk)+�2,k
⌘
zi,k |V i⇠G(4ai, �i,k)
with �i,k=aiP
k02Vv(k)zi,k0 and �i,k=(ai
Pk02Vz(k)
v�1i,k0)�1
pstandard distributions �! no acceptance/reject moves
.Numerical experiments-Scenario
- synthetic multifractal 2048⇥ 2048 image
�! 2D multifractal random walk (MRW)
-piece-wise constant values of c2 2 {�0.04,�0.02}- decomposition into non-overlapping 64⇥ 64 patches
- Illustration on a single realization
- estimates of c2
- k-means classification
-Estimation performance
evaluated on 100 realizations of heterogeneous 2D MRW
|bias| STD RMSE classification % time (s)
LF 0.0055 0.0406 0.0413 54.2 ⇠ 10IG 0.0018 0.0123 0.0125 76.5 ⇠ 50
GMRF 0.0027 0.0032 0.0044 94.6 ⇠ 50
Conclusion & Future workTake-away message- smooth joint estimation of c2 for image patches
-GMRF prior inducing positive correlation
- STD/RMSE reduced by ⇠ 4� 10 vs. state-of-the-art estimators
- e�cient MCMC algorithm (only ⇠ 5 times LF)
Future work- automatic estimation of ai- additional MF parameters (c1, c3, . . . )