Modify Manhattan Distance for Image Similarity

new measurement for image similarity

Authors

  • Moatasem Alsalih biology dept UPSI uni.
  • Kadhim M. Hashim Thi-Qar University, Faculty of Education for Pure Sciences Computer Department

DOI:

https://doi.org/10.31580/ojst.v2i4.984

Keywords:

Image Structural Similarity, Image Similarity, Mahattendistance, Standarddevation, Gaussian Noise

Abstract

A New measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (lowPSNR). The proposed measure, denoted by Manhattan distance and STD, uses a combination of two parts: the first part is the Geometric method, while the second part is based on the statistical feature. The concept of manhattan distance is used in the geometric part. The new measure shows the advantages of statistical approaches and geometric approaches. The proposed similarity method is an outcome for the human face. The novel measure outperforms the classical SSIM in detecting image similarity at low PSNR, with a significant difference in performance. AMS subject classification:

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 quality
measure: comparison with statistical similarity, Journal of Computer Science
10 (11) (2014).
[3] P. Premaratne, M. Premaratne, New structural similarity measure for image comparison,
Proceedings of the International Conference on Emerging Intelligent Computing
Technology and Applications, ICIC 2012, Huangshan, China (2012).
[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 SeventhWorkshop 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] Zhang, David; Jain, Anil (2006). Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5-7, 2006, Proceedings. Berlin: Springer Science+Business Media.

[8] Z.Wang, A. Bovik, Modren Image Quality Assessment, Morgan & Claypool Publishers,
2006 (2006).
[9] D. Lin, An information-theoretic definition of similarity, Proceedings of the Fifteenth
International Conference on Machine Learning (ICML’98) (1998).
[10] H. R. Mohammed and Z.M. Hussaing.acoorlative information theoritice measure for image similarity. (2017)
[11]S.K.Ali and Z.M.AydamGestures conversion to Arabic letters(2019)
[11] J. L. Rodgers,W.A.Nicewander, Thirteenways to look at the correlation coefficient,
The American Statistician 42 (1) (1988).
[12] Mahmoud A Muhsan Al-Dulami, “Feature-Based Face Recognition System Using Gabor Filter”, M.Sc. Thesis, University Of Technology, Informatics Institute For Postgraduate Studies, Iraq, 2005.
[13] Michel Marie Deza and Elena Deza, “ Encyclopedia of Distances”, Springer, p. 94,2009.
[14] Hermann K., “Real-Time Systems Design Principles for Distributed Embedded Applications”, Second edition, Springer,2011.
[15] Zhang, David; Jain, Anil (2006). Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5-7, 2006, Proceedings. Berlin: Springer Science+Business Media.
[16] V.Vijayakumari, “Face Recognition Techniques: A Survey”, World Journal of Computer Application and Technology, vol. 1, no. 2, pp. 41-50, 2013.
[17]SergiosTheodoridis And KonstantinosKoutroumbas, “Pattern Recognition”, Book, Elsevier Inc, 2009.
[18] Face94 Laboratories Cambridge, face94 face database http://www.cl.cam.ac.uk/research/dtg/attarchive/
facedatabase.html (2002).
[19] "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.
[20] ??????? ???????????? ??????? 2008
[21]R. Verma and J. Ali, "A comparative study of various types of image noise and efficient noise removal techniques," International Journal of advanced research in computer science and software engineering, vol. 3, no. 11, pp. 617-622, 2013.
[24] L. Spacek, University of essex, department of computer science, http://cswww.
essex.ac.uk/mv/allfaces/faces94.html.

[23] M. A. Farooque and J. S.Rohankar, "Survey on various noises and techniques for denoising the color image," International Journal ofApplication or Innovation in Engineering & Management (IJAIEM), vol. 2, no. 11, pp. 217-221, 2013.
[24] C. Saxena and P. D. Kourav, "Noises and Image Denoising Techniques: A Brief Survey," International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 3, pp. 878-885, 2014.

Downloads

Published

2020-02-01