Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32583
Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping

Authors: Adnan A. Y. Mustafa


In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.

Keywords: Big images, binary images, similarity, matching.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 828


[1] A. A. Mustafa, “Probabilistic Model for Quick Detection of Dissimilar Binary Images”. Journal of Electronic Imaging, 24, 5, 2015, pp. 24-53.
[2] A. A. Mustafa, “A Probabilistic Model for Random Binary Image Mapping”. WSEAS Transactions on Systems and Control, Volume 12, 2017, Art. #34, pp. 317-331.
[3] P. Jaccard, “Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines”. Bulletin de la Société Vaudoise des Sciences Naturelles, 1901, 37, pp. 241-272.
[4] R. Sokal and C. Michener, “A statistical method for evaluating systematic relationships”, Bulletin of the Society of University of Kansas, 1958, 38, pp. 1409-1438.
[5] R. Hamming, “Error detecting and error correcting codes”. Bell System Technical Journal, 1950, 29, (2), pp. 147–160.
[6] G. Sidorov et al., “Soft similarity and soft cosine measure: Similarity of features in vector space model”. Computación y Sistemas, 2014, 18, (3), pp. 491-504.
[7] P. Anuta, “Spatial Registration of Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform Techniques”. IEEE Transactions on Geoscience Electronics, GE-8, N 4, 1970, pp. 353-368.
[8] D. Barnea and H. Silverman, “A Class of Algorithms for Fast Digital Image Registration”. IEEE Trans. on Computers, Vol. c-21, N 2, 1972, pp.179-186.
[9] J. Pluim, A. Maintz and M. Viergever, “Mutual-Information-Based Registration of Medical Images: A Survey”. IEEE Transactions on Medical Imaging, 22, 8, 2003.
[10] A. A. Mustafa, “A Modified Hamming Distance Measure for Quick Rejection of Dissimilar Binary Images”. International Conference on Computer Vision and Image Analysis, 2015.
[11] S. Choi, S. Cha, and C. Tappert, ‘A Survey of Binary Similarity and Distance Measures’. Journal of Systems, Cybernetics and Informatics, 2010, 8, (1), pp. 43-48.
[12] A. A. Mustafa, “Quick Probabilistic Binary Image Matching: Changing the Rules of the Game”. Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997112 (September 27, 2016); doi:10.1117/12.2237552.
[13] A. A. Mustafa, “A Probabilistic Binary Similarity Distance for Quick Image Matching”. IET Journal on Image Processing, submitted for review.