Content-based Image Retrieval Using Gabor Texture Features Dengsheng Zhang, Aylwin Wong, Maria Indrawan, Guojun Lu Gippsland School of Computing and Information Technology Monash University Churchill, Victoria, 3842, Australia Email: {dengsheng.zhang, aylwin.wong, maria.indrawan, guojun.lu}@infotech.monash.edu.au
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Content-based Image Retrieval Using Gabor Texture Features
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Content-based Image Retrieval Using Gabor
Texture Features
Dengsheng Zhang, Aylwin Wong, Maria Indrawan, Guojun Lu
Gippsland School of Computing and Information Technology Monash University
Churchill, Victoria, 3842, Australia Email: dengsheng.zhang, aylwin.wong, maria.indrawan,
• Difficult to model. (No formal definition of texture exists, intuitively texture provides measures of properties such as smoothness, coarseness and regularity etc.)
• Tamura textures. (coarseness, contrast, directionality, line-likeness, regularity and roughness.)
Texture Analysis
• Structural. Textures composed of a number of copies of a primitive placed at various locations in the image plane
Gabor texture analysis joint-space approach (among the best method)
• Psychological evidence Visual discrimination is a local process. Pattern grouping depends on both shape similarity (spatial properties) and organization (spectral properties), this leads to the need for high resolution in both the spatial and frequency domains. [Reed & Wechsler90]
• Theoretical evidence achieve lower
boundary of uncertainty in joint-space • Experimental evidence
Outperforms most other methods[Tan92, Manjunath & Ma96] • Biological evidence 2D Gabor filters
best describe the 2D receptive-field profiles of simple cells found in the visual cortex of vertebrate animals [Daugman 85, Wiskott99], therefore, they are consistent with human vision system (HSV) [Reed & Wechsler90]
Gabor Filters IDefinition
Gabor filters are a group of wavelets. For a given image I(x, y) with size P××××Q, its discrete Gabor wavelet transform is given by a convolution:
∑∑ −−=s t
mnmn tstysxIyxG ),(),(),( *ψ
where, s and t are the filter mask size variables, and
*mnψ is the complex
conjugate of ψψψψmn.
Gabor Filters IIWavelet Generation
A. Mother Wavelet
Gabor impulse response function, a Gaussian envelope modulated by a sinusoidal plane wave along the x-axis:
)2exp()](21exp[
21),( 2
2
2
2
Wxjyxyxyxyx
πσσσπσ
ψ ⋅+−=
where W is called the modulation frequency.
Transfer function of Mother Wavelet -- frequency response of Gabor function ψψψψ(x, y), i.e., its 2-D Fourier transform is:
])([21exp),( 2
2
2
2
vu
vWuvuσσ
+−−=Ψ
where σσσσu = (2ππππσσσσx)-1 and σσσσv = (2ππππσσσσy)-1.
Gabor Filters
NotationsGabor Transform and Short-time Fourier Transform (SFT)
• Fourier transform:
∫∫ +−= dydxvyuxjyxIvuF )](2exp[),(),( π (localization in frequency domain only)
Magnitude of a Gabor filter set for N=4 in direction of the modulation frequency
Half-peak plot of the Gabor filters in the frequency plane tuned to different frequencies (with bandwith of Uh -Ul) and orientations (30 degree resolution)
Implementation of Gabor filters
• Parameters selection
m = 5 (scales), n = 6 (orientations), Ul= 0.05, Uh=0.4, s and t range form 0 to 60, i.e, filter mask size is 60x60.
• Convolution
∫∫ −−−= dtdswxjtysxgyxIwG yx )2exp(),(),()(, π
F (I ⊗⊗⊗⊗ G ) = F ( I ) · F (G )
Experimental results
Gabor texture feature & similarity measure
• Gabor texture feature
After applying Gabor filters on the image with different orientation at different scale, we obtain an array of magnitudes:
∑∑=x y
mn yxGnmE |),(|),(, m = 0, 1, , M-1; n = 0, 1, , N-1
Figure 1. (a) a straw image, (b) energy map of (a), (c) rotated image of (a), (d) energy map of (c)
Rotation Invariance-II
Assume the original image is I(x, y) with dominant orientation at iππππ/N. I'(x, y) is the rotate version of I(x, y) so that its dominant orientation is at 0. If at a particular scale m, the energy distribution of I(x, y) is
(E m,0, E m,1, … E m,i, …E m,N-1)
then the energy distribution of I'(x, y) is
(E'm,-i, E' m,1-i, … E' m,0, …E' m,N-1-i)
where E m,0= E' m,-i, E m,1= E' m, 1-i, and so forth. Because E' m,n = E' m,n+N (an image has the same energy distribution after rotating 180o.), we have E' m,-i+N =E' m,-i, E' m,1-i+N = E'm, 1-i, etc. (Negative orientations are added by N). We then have the following energy distribution of I'(x, y):
(E'm,-i+N, E'm,1-i+N,…,E'm,0 ,…,E'm,N-1-i) Reorder the above distribution according to orientation values
(E'm,0 , E'm,1 ,…E'm,N-1-i , E'm,N-i , E'm,N-i+1…,E'm,N-1) this is the circular rotation of the original feature vector
Retrieval Test
• Database creation 112 512××××512 Brodatz texture images, each is cut into 16 128××××128
sub textures, to create a database composed of 1792 textures, plus some deliberately rotated textures.