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A Correlative Information-Theoretic Measure
for Image Similarity
By:
Farah M. Altufaili
MSc Candidate in Computer Science, University of Kufa, Najaf, Iraq
farahaltofili@yahoo.com
Hind Rustum Mohammed,
Professor, Faculty of Computer Science & Math, University of Kufa, Najaf, Iraq
hindrustum.shaaban@uokufa.edu.iq
Zahir M. Hussain
Professor, Faculty of Computer Science & Math, University of Kufa, Najaf, Iraq
Professor, School of Engineering, Edith Cowan University, Joondalup, Australia
Senior Member, IEEE
zahir.hussain@uokufa.edu ; z.hussain@ecu.edu.au ; zmhussain@ieee.org
Published in:
Global Journal of Pure and Applied Mathematics
Volume 13, Number 2, 2017
Global Journal of Pure and Applied Mathematics.
ISSN 0973-1768 Volume 13, Number 2 (2017), pp. 159–169
© Research India Publications
http://www.ripublication.com/gjpam.htm
A Correlative Information-Theoretic Measure
for Image Similarity
Farah M. Altufaili
Faculty of Computer Science & Mathematics,
University of Kufa, Najaf, Iraq
Hind Rustum Mohammed
Faculty of Computer Science & Mathematics,
University of Kufa, Najaf, Iraq
Zahir M. Hussain1
Faculty of Computer Science & Mathematics,
University of Kufa, Najaf, Iraq.
Professor, School of Engineering,
Edith Cowan University, Joondalup, Australia.
E-mail: zmhussain@ieee.org; z.hussain@ecu.edu.au
Abstract
A hybrid measure is proposed for assessing the similarity among gray-scale images.
The well-known Structural Similarity Index Measure (SSIM) has been designed us-
ing a statistical approach that fails under significant noise (low PSNR).The proposed
measure, denoted by SjhCorr2, uses a combination of two parts: the first part is in-
formation - theoretic, while the second part is based on 2D correlation. The concept
of symmetric joint histogram is used in the information - theoretic part. The new
measure shows the advantages of statistical approaches and information - theoretic
approaches. The proposed similarity approach is robust under noise. The new
measure outperforms the classical SSIM in detecting image similarity at low PSNR
under Gaussian and impulse noise, with significant difference in performance.
AMS subject classification:
Keywords: Joint histogram, image structural similarity, information-theoretic sim-
ilarity, image similarity, Gaussian noise, impulse noise, 2D correlation.
1
Corresponding author.
160 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain
1. Introduction
In image processing, applications that require comparing two images according to their
content, image matching is an essential component in this process. One of the most
important examples is the image database retrieval systems [1]. Image similarity has
become in the recent years a basic point in image processing applications like monitoring,
image compression, restoration, and many other application.
Image similarity can be defined as the difference between two images, and image
similarity measure is a numerical difference between two different images under com-
parison. Similarity techniques can be classified according to the methods they use in
deriving or defining the difference. The first kind of techniques is the statistical - based
methods, and the second important type is the information - theoretical techniques [2].
An old statistical measure that has been widely used to detect image similarity is the
mean squared error (MSE) [3, 4, 5].
Recently, light has been shed on a new measure that coincides with the HumanVisual
System (HSV): Structural Similarity Index Measure (SSIM) is proposed in 2004 byWang
and Bovik. SSIM proved to be distinguished due to its notable performance as compared
to the previous metrics [1, 6].
Image recognition has become an interesting subject for researchers over the past
two decades because of its potential applications in many important fields like character
recognition, human computer interfaces, identity authentication and video surveillance.
Different methods for image recognition, mostly for face image recognition, have
been proposed [7]. Many algorithms of face and object recognition systems have recently
been designed based on image similarity measure like SSIM [1].
In this work we propose a hybrid similarity measure that combines features of 2D
correlation and the information - theoretic features as represented by a new formula
for joint histogram. The proposed measure is shown to outperform SSIM in detecting
similarity under Gaussian and impulsive noise.
The paper is organized as follows: Section 2 deals with SSIM, which is the well-
known structural similarity, and information - theoretic measures. Section 3 presents a
method that applies a formula for symmetric joint histogram. In Section 4, experimental
results are presented; along with performance measures. The conclusions of this study
are given in Section 5.
2. Methodology
There have been two major approaches for image similarity: statistical approaches and
information - theoretic approaches.
Statistical methods:
Mean-squared error (MSE) is a well-known statistical measure. However, MSE is too
weak for modern applications of image processing like face recognition. The first signif-
icant structural similarity measure, called Structural Similarity Index Measure (SSIM),
A Correlative Information-Theoretic Measure For Image Similarity 161
has been proposed in 2004 [1]. SSIM used statistical image parameters such as mean,
variance, co-variance, and standard deviation as follows [1, 8]:
ρ(x, y) =
(2µxµy + C1)(2σxy + C2)
(µ2
x + µ2
y + C1)(σ2
x + σ2
y + C2)
(1)
where ρ(x, y) is the SSIM metric between images x and y, while µx, µy, σ2
x and σ2
y are
the statistical means and variances of x and y, respectively; σxy is the covariance of x
and y, and finally the constants C1 and C2 are inserted to avoid unstable results that may
be reached due to division by zero, and are defined as C1 = (K1L)2
and C2 = (K2L)2
,
with K1 and K2 are small constants and L = 255 (maximum pixel value).
Information - theoretic methods:
In information - theoretic approach, the similarity between x and y (where x and y are
images) can be defined as the corresponding differences between information - theoretic
features of the two images. The more differences they have, the less similar they are [9].
In 2013, D. Mistry, A. Banerjee and A. Tatu proposed a new similarity measure that
base on joint entropy (joint histogram) [10]. The proposed measure is based on the fact of
the joint entropy is the measure of uncertainty among two images, so if the joint entropy
is low then the similarity between two images are high, and vice-versa. The joint entropy
was first applied on two compared images using joint histogram as in [9].
In 2014, A. F. Hassan, D. Cai-lin and Z. M. Hussain proposed a new measure called
HSSIM that based on joint histogram. HSSIM outperforms statistical similarity of SSIM;
it has the ability to detect similarity under significant noise (low PSNR), with an average
difference of nearly 20 dB with SSIM [2].
3. A Hybrid Measure
Asymmetricformulaforjointhistogramisutilizedheretocreateanewmeasure, designed
to be a hybrid measure combining statistical features (represented by 2D correlation)
with information - theoretic features. The new measure is a combination of two parts as
follows.
The first part of the proposed measure is the information - theoretic part that uses the
concept of symmetric joint histogram, defined as follows:
Q(x, y) =
i j (Tij − τij ) 1
hi−C
2
2L2
(2)
where Tij is the symmetric joint histogram of image x and image y, τij = T (x, x) is
the self- symmetric joint histogram of the first (reference) image x, hi is the original
histogram of x and C is a very small positive constant to avoid division by zero. Note
that:
Q(x, y) ≥ 0
162 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain
The above value can be normalized by using maximal error estimated value Q∞(x, y)
in significant noise (very low PSNR) as follows:
v(x, y) =
Q(x, y)
Q∞(x, y)
(3)
The process of normalization will ensure that:
0 ≤ v(x, y) ≤ 1
The final version of the first part can be stated as follows:
q(x, y) = 1 − v(x, y)
The second part of the measure is represented by 2D correlation between reference image
x and noisy image y as follows:
p(x, y) = corr2(x, y) (4)
where corr2 is given as follows [11]:
corr2(x, y) =
i j [x(i, j) − ¯x] · [y(i, j) − ¯y]
[ i j (x(i, j) − ¯x)2] · [ i j (y(i, j) − ¯y)2]
(5)
where ¯x and ¯y are the mean values of x and y respectively.
The effect of information-theoretic features could be incorporated with the effect of
correlative features as follows:
SjhCorr2(x, y) =
(k1 + k3) · q + k2 · p
k2 · p + k3 · q + k1
(6)
where k1, k2, k3 are constants.
Note that 0 ≤ SjhCorr2 ≤ 1; giving 1 for completely similar images and 0 for
completely different images.
The above hybrid measure can be calculated according to the following algorithm.
Algorithm:
Input:
Images x and y, which are the reference image and the noisy version, C is small constant
and L = 255 which represents the maximum pixel value.
Output:
Similarity, a number ranging between 0 and 1.
Step 1: Convert image values into double type.
Step 2: Set p = corr2(x, y)
Step 3: Set τij = self-symmetric joint histogram of x.
Step 4: Set Tij = symmetric joint histogram of x and y.
A Correlative Information-Theoretic Measure For Image Similarity 163
Step 5: Set Q:
Q(x, y) =
i j (Tij − τij ) 1
hi−C
2
2L2
Step 6: Set Q∞ = Q(x, y) when noise is maximum.
Step 7: Set q after normalization: v = Q/Q∞; q = 1 − v.
Step 8: Compute SjhCorr2(x, y) =
(k1 + k3) · q + k2 · p
k2 · p + k3 · q + k1
; where k1, k2, k3 are con-
stants.
End of Algorithm.
4. Test Environment
Two types of noise have been considered in simulation and testing: Gaussian noise,
which is one of the most popular noise types that are encountered in signal processing
systems; and impulsive noise, which is common in image processing.
To test the performance of the proposed measure, different categories of images have
been considered: a human face (from AT&T database, [12]), a geometric shape, and a
landscape. Parameters are k1 = 3, k2 = 2, k3 = 4.
5. Results and Discussion
The proposed measure has been tested and simulated using MATLAB.
A. Performance under Gaussian Noise:
The proposed measure has been tested under Gaussian noise, which is the most popular
noise that attacks images and systems. Results are shown in Figures 1-3. Table 1 shows a
comparison between the SSIM and SjhCorr2 for different types of images. Comparison
has also been made with joint histogram similarity, Sjh. The proposed measure gives
larger similarity than SSIM under Gaussian noise.
Table 1: SJHCORR2 vs. SSIM under Gaussian noise (PSNR = 28 dB).
XXXXXXXXXXXXImage
Measure
SSIM SJHCORR2
Landscape 0.4181 0.9906
Human Face 0.7239 0.9940
Geometric Shape 0.2556 0.9797
164 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain
Figure 1: Performance comparison of SSIM and SjhCorr2 using similar images (land-
scape image) under Gaussian noise.
B. Performance under Impulsive Noise:
Second test was performed under impulsive noise, salt & pepper model from Matlab has
been used. Results are shown in Table 2 and Figures 4-6.
It can be seen that the proposed measure gives better results (larger similarity between
the image and its noisy version) under impulsive noise for various kinds of images
(geometric, human face, and landscape images).
A Correlative Information-Theoretic Measure For Image Similarity 165
Figure 2: Performance comparison of SSIM and SjhCorr2 using similar images (human
face) under Gaussian noise.
Table 2: SJHCORR2 vs. SSIM under impulsive noise (PSNR = 10 dB).
XXXXXXXXXXXXImage
Measure
SSIM SJHCORR2
Landscape 0.0450 0.6685
Human Face 0.0960 0.6958
Geometric Shape 0.0484 0.6700
166 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain
Figure 3: Performance comparison of SSIM and SjhCorr2 using similar images (geo-
metric image) under Gaussian noise.
A Correlative Information-Theoretic Measure For Image Similarity 167
Figure 4: Performance of SSIM and SjhCorr2 using similar images (landscape) under
impulsive noise.
Figure 5: Performance of SSIM and SjhCorr2 using similar images (human face) under
impulsive noise.
168 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain
Figure 6: Performance of SSIM and SjhCorr2 using similar images (geometric) under
impulsive noise.
6. Conclusions
An image hybrid similarity measure, called symmetric joint histogram with 2D correla-
tion (SjhCorr2), has been proposed and tested versus the well-known structural similarity
measure (SSIM) under Gaussian noise and impulsive noise (salt and pepper model from
MATLAB). Design of the proposed method is based on a combination of information-
theoretic and statistical features. A symmetric formula for the joint histogram has been
used as information - theoretic tool, and image 2D correlation has been used as a statis-
tical tool. The new measure gave better performance (more similarity) than the existing
statistical-based SSIM under noisy conditions.
Acknowledgements
TheAuthors would like to thank the Ministry of Higher Education & Scientific Research
(Iraq) and the School of Engineering, Edith Cowan University (Australia) for financial
support of this project.
References
[1] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, Image quality assessment: From
error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004).
[2] A. F. Hassan, D. Cai-lin, Z. M. Hussain, An information-theoretic image qual-
ity measure: comparison with statistical similarity, Journal of Computer Science
10 (11) (2014).
[3] P. Premaratne, M. Premaratne, New structural similarity measure for image com-
parison, Proceedings of the International Conference on Emerging Intelligent Com-
puting Technology and Applications, ICIC 2012, Huangshan, China (2012).
A Correlative Information-Theoretic Measure For Image Similarity 169
[4] A. M. Eskicioglu, Quality measurement for monochrome compressed images in
the past 25 years, IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP2000) (2000).
[5] B. Girod, Psychovisual aspects of image processing: What’s wrong with mean
squared error?, Proceedings of the Seventh Workshop on Multidimensional Signal
Processing (1991).
[6] J. Goldberger, S. Gordon, H. Greenspan, An efficient image similarity measure
based on approximations of kl-divergence between two gaussian mixtures, IEEE
International Conference on Computer Vision (ICCV) (2003).
[7] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, Face recognition: a literature
survey, ACM Computing Surveys (2003).
[8] Z. Wang, A. Bovik, Modren Image Quality Assessment, Morgan & Claypool Pub-
lishers, 2006 (2006).
[9] D. Lin, An information-theoretic definition of similarity, Proceedings of the Fif-
teenth International Conference on Machine Learning (ICML’98) (1998).
[10] D. Mistry, A. Banerjee, A. Tatu, Image similarity based on joint entropy (joint
histogram), International Conference on Advances in Engineering and Technology
(ICAET–2013) (2013).
[11] J.L.Rodgers,W.A.Nicewander,Thirteenwaystolookatthecorrelationcoefficient,
The American Statistician 42 (1) (1988).
[12] AT & T Laboratories Cambridge, AT & T face database (formerly ‘the
ORL Database of Faces’), http://www.cl.cam.ac.uk/research/dtg/attarchive/
facedatabase.html (2002).

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A Correlative Information-Theoretic Measure for Image Similarity

  • 1. A Correlative Information-Theoretic Measure for Image Similarity By: Farah M. Altufaili MSc Candidate in Computer Science, University of Kufa, Najaf, Iraq farahaltofili@yahoo.com Hind Rustum Mohammed, Professor, Faculty of Computer Science & Math, University of Kufa, Najaf, Iraq hindrustum.shaaban@uokufa.edu.iq Zahir M. Hussain Professor, Faculty of Computer Science & Math, University of Kufa, Najaf, Iraq Professor, School of Engineering, Edith Cowan University, Joondalup, Australia Senior Member, IEEE zahir.hussain@uokufa.edu ; z.hussain@ecu.edu.au ; zmhussain@ieee.org Published in: Global Journal of Pure and Applied Mathematics Volume 13, Number 2, 2017
  • 2. Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 13, Number 2 (2017), pp. 159–169 © Research India Publications http://www.ripublication.com/gjpam.htm A Correlative Information-Theoretic Measure for Image Similarity Farah M. Altufaili Faculty of Computer Science & Mathematics, University of Kufa, Najaf, Iraq Hind Rustum Mohammed Faculty of Computer Science & Mathematics, University of Kufa, Najaf, Iraq Zahir M. Hussain1 Faculty of Computer Science & Mathematics, University of Kufa, Najaf, Iraq. Professor, School of Engineering, Edith Cowan University, Joondalup, Australia. E-mail: zmhussain@ieee.org; z.hussain@ecu.edu.au Abstract A hybrid measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed us- ing a statistical approach that fails under significant noise (low PSNR).The proposed measure, denoted by SjhCorr2, uses a combination of two parts: the first part is in- formation - theoretic, while the second part is based on 2D correlation. The concept of symmetric joint histogram is used in the information - theoretic part. The new measure shows the advantages of statistical approaches and information - theoretic approaches. The proposed similarity approach is robust under noise. The new measure outperforms the classical SSIM in detecting image similarity at low PSNR under Gaussian and impulse noise, with significant difference in performance. AMS subject classification: Keywords: Joint histogram, image structural similarity, information-theoretic sim- ilarity, image similarity, Gaussian noise, impulse noise, 2D correlation. 1 Corresponding author.
  • 3. 160 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain 1. Introduction In image processing, applications that require comparing two images according to their content, image matching is an essential component in this process. One of the most important examples is the image database retrieval systems [1]. Image similarity has become in the recent years a basic point in image processing applications like monitoring, image compression, restoration, and many other application. Image similarity can be defined as the difference between two images, and image similarity measure is a numerical difference between two different images under com- parison. Similarity techniques can be classified according to the methods they use in deriving or defining the difference. The first kind of techniques is the statistical - based methods, and the second important type is the information - theoretical techniques [2]. An old statistical measure that has been widely used to detect image similarity is the mean squared error (MSE) [3, 4, 5]. Recently, light has been shed on a new measure that coincides with the HumanVisual System (HSV): Structural Similarity Index Measure (SSIM) is proposed in 2004 byWang and Bovik. SSIM proved to be distinguished due to its notable performance as compared to the previous metrics [1, 6]. Image recognition has become an interesting subject for researchers over the past two decades because of its potential applications in many important fields like character recognition, human computer interfaces, identity authentication and video surveillance. Different methods for image recognition, mostly for face image recognition, have been proposed [7]. Many algorithms of face and object recognition systems have recently been designed based on image similarity measure like SSIM [1]. In this work we propose a hybrid similarity measure that combines features of 2D correlation and the information - theoretic features as represented by a new formula for joint histogram. The proposed measure is shown to outperform SSIM in detecting similarity under Gaussian and impulsive noise. The paper is organized as follows: Section 2 deals with SSIM, which is the well- known structural similarity, and information - theoretic measures. Section 3 presents a method that applies a formula for symmetric joint histogram. In Section 4, experimental results are presented; along with performance measures. The conclusions of this study are given in Section 5. 2. Methodology There have been two major approaches for image similarity: statistical approaches and information - theoretic approaches. Statistical methods: Mean-squared error (MSE) is a well-known statistical measure. However, MSE is too weak for modern applications of image processing like face recognition. The first signif- icant structural similarity measure, called Structural Similarity Index Measure (SSIM),
  • 4. A Correlative Information-Theoretic Measure For Image Similarity 161 has been proposed in 2004 [1]. SSIM used statistical image parameters such as mean, variance, co-variance, and standard deviation as follows [1, 8]: ρ(x, y) = (2µxµy + C1)(2σxy + C2) (µ2 x + µ2 y + C1)(σ2 x + σ2 y + C2) (1) where ρ(x, y) is the SSIM metric between images x and y, while µx, µy, σ2 x and σ2 y are the statistical means and variances of x and y, respectively; σxy is the covariance of x and y, and finally the constants C1 and C2 are inserted to avoid unstable results that may be reached due to division by zero, and are defined as C1 = (K1L)2 and C2 = (K2L)2 , with K1 and K2 are small constants and L = 255 (maximum pixel value). Information - theoretic methods: In information - theoretic approach, the similarity between x and y (where x and y are images) can be defined as the corresponding differences between information - theoretic features of the two images. The more differences they have, the less similar they are [9]. In 2013, D. Mistry, A. Banerjee and A. Tatu proposed a new similarity measure that base on joint entropy (joint histogram) [10]. The proposed measure is based on the fact of the joint entropy is the measure of uncertainty among two images, so if the joint entropy is low then the similarity between two images are high, and vice-versa. The joint entropy was first applied on two compared images using joint histogram as in [9]. In 2014, A. F. Hassan, D. Cai-lin and Z. M. Hussain proposed a new measure called HSSIM that based on joint histogram. HSSIM outperforms statistical similarity of SSIM; it has the ability to detect similarity under significant noise (low PSNR), with an average difference of nearly 20 dB with SSIM [2]. 3. A Hybrid Measure Asymmetricformulaforjointhistogramisutilizedheretocreateanewmeasure, designed to be a hybrid measure combining statistical features (represented by 2D correlation) with information - theoretic features. The new measure is a combination of two parts as follows. The first part of the proposed measure is the information - theoretic part that uses the concept of symmetric joint histogram, defined as follows: Q(x, y) = i j (Tij − τij ) 1 hi−C 2 2L2 (2) where Tij is the symmetric joint histogram of image x and image y, τij = T (x, x) is the self- symmetric joint histogram of the first (reference) image x, hi is the original histogram of x and C is a very small positive constant to avoid division by zero. Note that: Q(x, y) ≥ 0
  • 5. 162 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain The above value can be normalized by using maximal error estimated value Q∞(x, y) in significant noise (very low PSNR) as follows: v(x, y) = Q(x, y) Q∞(x, y) (3) The process of normalization will ensure that: 0 ≤ v(x, y) ≤ 1 The final version of the first part can be stated as follows: q(x, y) = 1 − v(x, y) The second part of the measure is represented by 2D correlation between reference image x and noisy image y as follows: p(x, y) = corr2(x, y) (4) where corr2 is given as follows [11]: corr2(x, y) = i j [x(i, j) − ¯x] · [y(i, j) − ¯y] [ i j (x(i, j) − ¯x)2] · [ i j (y(i, j) − ¯y)2] (5) where ¯x and ¯y are the mean values of x and y respectively. The effect of information-theoretic features could be incorporated with the effect of correlative features as follows: SjhCorr2(x, y) = (k1 + k3) · q + k2 · p k2 · p + k3 · q + k1 (6) where k1, k2, k3 are constants. Note that 0 ≤ SjhCorr2 ≤ 1; giving 1 for completely similar images and 0 for completely different images. The above hybrid measure can be calculated according to the following algorithm. Algorithm: Input: Images x and y, which are the reference image and the noisy version, C is small constant and L = 255 which represents the maximum pixel value. Output: Similarity, a number ranging between 0 and 1. Step 1: Convert image values into double type. Step 2: Set p = corr2(x, y) Step 3: Set τij = self-symmetric joint histogram of x. Step 4: Set Tij = symmetric joint histogram of x and y.
  • 6. A Correlative Information-Theoretic Measure For Image Similarity 163 Step 5: Set Q: Q(x, y) = i j (Tij − τij ) 1 hi−C 2 2L2 Step 6: Set Q∞ = Q(x, y) when noise is maximum. Step 7: Set q after normalization: v = Q/Q∞; q = 1 − v. Step 8: Compute SjhCorr2(x, y) = (k1 + k3) · q + k2 · p k2 · p + k3 · q + k1 ; where k1, k2, k3 are con- stants. End of Algorithm. 4. Test Environment Two types of noise have been considered in simulation and testing: Gaussian noise, which is one of the most popular noise types that are encountered in signal processing systems; and impulsive noise, which is common in image processing. To test the performance of the proposed measure, different categories of images have been considered: a human face (from AT&T database, [12]), a geometric shape, and a landscape. Parameters are k1 = 3, k2 = 2, k3 = 4. 5. Results and Discussion The proposed measure has been tested and simulated using MATLAB. A. Performance under Gaussian Noise: The proposed measure has been tested under Gaussian noise, which is the most popular noise that attacks images and systems. Results are shown in Figures 1-3. Table 1 shows a comparison between the SSIM and SjhCorr2 for different types of images. Comparison has also been made with joint histogram similarity, Sjh. The proposed measure gives larger similarity than SSIM under Gaussian noise. Table 1: SJHCORR2 vs. SSIM under Gaussian noise (PSNR = 28 dB). XXXXXXXXXXXXImage Measure SSIM SJHCORR2 Landscape 0.4181 0.9906 Human Face 0.7239 0.9940 Geometric Shape 0.2556 0.9797
  • 7. 164 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain Figure 1: Performance comparison of SSIM and SjhCorr2 using similar images (land- scape image) under Gaussian noise. B. Performance under Impulsive Noise: Second test was performed under impulsive noise, salt & pepper model from Matlab has been used. Results are shown in Table 2 and Figures 4-6. It can be seen that the proposed measure gives better results (larger similarity between the image and its noisy version) under impulsive noise for various kinds of images (geometric, human face, and landscape images).
  • 8. A Correlative Information-Theoretic Measure For Image Similarity 165 Figure 2: Performance comparison of SSIM and SjhCorr2 using similar images (human face) under Gaussian noise. Table 2: SJHCORR2 vs. SSIM under impulsive noise (PSNR = 10 dB). XXXXXXXXXXXXImage Measure SSIM SJHCORR2 Landscape 0.0450 0.6685 Human Face 0.0960 0.6958 Geometric Shape 0.0484 0.6700
  • 9. 166 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain Figure 3: Performance comparison of SSIM and SjhCorr2 using similar images (geo- metric image) under Gaussian noise.
  • 10. A Correlative Information-Theoretic Measure For Image Similarity 167 Figure 4: Performance of SSIM and SjhCorr2 using similar images (landscape) under impulsive noise. Figure 5: Performance of SSIM and SjhCorr2 using similar images (human face) under impulsive noise.
  • 11. 168 Farah M. Altufaili, Hind Rustum Mohammed, and Zahir M. Hussain Figure 6: Performance of SSIM and SjhCorr2 using similar images (geometric) under impulsive noise. 6. Conclusions An image hybrid similarity measure, called symmetric joint histogram with 2D correla- tion (SjhCorr2), has been proposed and tested versus the well-known structural similarity measure (SSIM) under Gaussian noise and impulsive noise (salt and pepper model from MATLAB). Design of the proposed method is based on a combination of information- theoretic and statistical features. A symmetric formula for the joint histogram has been used as information - theoretic tool, and image 2D correlation has been used as a statis- tical tool. The new measure gave better performance (more similarity) than the existing statistical-based SSIM under noisy conditions. Acknowledgements TheAuthors would like to thank the Ministry of Higher Education & Scientific Research (Iraq) and the School of Engineering, Edith Cowan University (Australia) for financial support of this project. References [1] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004). [2] A. F. Hassan, D. Cai-lin, Z. M. Hussain, An information-theoretic image qual- ity measure: comparison with statistical similarity, Journal of Computer Science 10 (11) (2014). [3] P. Premaratne, M. Premaratne, New structural similarity measure for image com- parison, Proceedings of the International Conference on Emerging Intelligent Com- puting Technology and Applications, ICIC 2012, Huangshan, China (2012).
  • 12. A Correlative Information-Theoretic Measure For Image Similarity 169 [4] A. M. Eskicioglu, Quality measurement for monochrome compressed images in the past 25 years, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2000) (2000). [5] B. Girod, Psychovisual aspects of image processing: What’s wrong with mean squared error?, Proceedings of the Seventh Workshop on Multidimensional Signal Processing (1991). [6] J. Goldberger, S. Gordon, H. Greenspan, An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures, IEEE International Conference on Computer Vision (ICCV) (2003). [7] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, Face recognition: a literature survey, ACM Computing Surveys (2003). [8] Z. Wang, A. Bovik, Modren Image Quality Assessment, Morgan & Claypool Pub- lishers, 2006 (2006). [9] D. Lin, An information-theoretic definition of similarity, Proceedings of the Fif- teenth International Conference on Machine Learning (ICML’98) (1998). [10] D. Mistry, A. Banerjee, A. Tatu, Image similarity based on joint entropy (joint histogram), International Conference on Advances in Engineering and Technology (ICAET–2013) (2013). [11] J.L.Rodgers,W.A.Nicewander,Thirteenwaystolookatthecorrelationcoefficient, The American Statistician 42 (1) (1988). [12] AT & T Laboratories Cambridge, AT & T face database (formerly ‘the ORL Database of Faces’), http://www.cl.cam.ac.uk/research/dtg/attarchive/ facedatabase.html (2002).