New Distance Measurements for Image SimilarityNew Distance Measurements for Image Similarity
AbstractNew measures have been proposed for assessing the similarity of gray-level images. The famous structural
similarity index measurement (SSIM) has been designed using statistical approach that fails with high
noise (lowPSNR). The two proposed measures have been suggested, the first one depend on Manhattan
distance and standard division, this measure combined from two parts: the first part depend on the
Manhattan distance which is used in geometric features the second part is based on statistical feature. The
second measure utilized the modification of Euclidian distant. The two proposed similarity measures are
outcome for human face. The new measures outperform the classical SSIM in detecting image similarity
at low PSNR, with significant difference in performance. The results were (95.3% for the first
measurement) and (99.2% for the second measurement). We used database Face94
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from
error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.
Hassan, A. F., Cailin, D., & Hussain, Z. M. (2014). An information-theoretic image quality measure:
Comparison with statistical similarity.
Premaratne, P., & Premaratne, M. (2012, July). New structural similarity measure for image
comparison. In International Conference on Intelligent Computing (pp. 292-297). Springer, Berlin,
Eskicioglu, A. M. (2000, June). Quality measurement for monochrome compressed images in the past
years. In 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing.
Proceedings (Cat. No. 00CH37100) (Vol. 4, pp. 1907-1910). IEEE.
Girod, B. (1993). What's wrong with mean-squared error? Digital images and human vision, 207-220.
Goldberger, J., Gordon, S., & Greenspan, H. (2003, October). An efficient image similarity measure
based on approximations of KL-divergence between two Gaussian mixtures. In null (p. 487). IEEE.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature
survey. ACM computing surveys (CSUR), 35(4), 399-458.  Lee, S. W., & Li, S. Z. (Eds.).
(2007). Advances in Biometrics: International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007,
Proceedings (Vol. 4642). Springer.
Wang, Z., & Bovik, A. C. (2006). Modern image quality assessment. Synthesis Lectures on Image,
Video, and Multimedia Processing, 2(1), 1-156.
Lin, D. (1998, July). An information-theoretic definition of similarity. In Icml (Vol. 98, No. 1998, pp.
H. R. Mohammed and Z.M. Hussaing. A correlative information theoretic measure for image
Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation
coefficient. The American Statistician, 42(1), 59-66.
AL-Dulami, M. A. M. (2005). Feature-Based Face Recognition System Using Gabor
Filter (Doctoral dissertation, M. Sc Thesis, Department of Computer Sciences of the University of
Deza, M. M., & Deza, E. (2009). Encyclopedia of distances. In Encyclopedia of distances (pp. 1-
. Springer, Berlin, Heidelberg.
Christopher C., James N. (2009), Oxford Concise Dictionary of Mathematics”, OUP oxford.
Kopetz, H. (2011). Real-time systems: design principles for distributed embedded applications.
Springer Science & Business Media.
Zhang, D. Y., & Jain, A. K. (Eds.). (2005). Advances in Biometrics: International Conference, ICB
, Hong Kong, China, January 5-7, 2006, Proceedings (Vol. 3832). Springer..
Vijayakumari, V. (2013). Face recognition techniques: A survey. World journal of computer
application and technology, 1(2), 41-50.
Theodoridis, S., & Koutroumbas, K. (2009). livro: Pattern Recognition.
Face94 Laboratories Cambridge, face94 face database
A. F. Hassan, D. Cai-lin and Z. M. Hussain, "An Information-Theoretic Image Quality Measure:
Comparison with Statistical Similarity," Journal of Computer Science, 2014.
"The Directed Distance" (PDF). Information and Telecommunication Technology Center. University
of Kansas. Archived from the original (PDF) on 10 November 2016. Retrieved 18 September 2018.
Verma, R., & Ali, J. (2013). A comparative study of various types of image noise and efficient noise
removal techniques. International Journal of advanced research in computer science and software
L. Spacek, University of essex, department of computer science, http://cswww.
Farooque, M. A., & Rohankar, J. S. (2013). Survey on various noises and techniques for denoising
the color image. International Journal of Application or Innovation in Engineering & Management
(IJAIEM), 2(11), 217-221.
C. Saxena and P. D. Kourav, “Noises and Image Denoising Techniques: A Brief Survey,”
International Journal of Emerging Technology and Advanced Engineering, 2014
Copyright (c) 2020 Journal of Education for Pure Science- University of Thi-Qar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Journal of education for Pure Science (Jeds), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (Jeds), University of Thi-Qar.
Journal of education for Pure Science (Jeds), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (Jeds), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.