Abstract
The problem considered in this paper is how to measure resemblance between images. One approach to the solution to this problem is to find parts of images that resemble each other with a tolerable level of error. This leads to a consideration of tolerance relations that define coverings of images and measurement of the degree of overlap between tolerances classes in pairs of images. This approach is based on a tolerance class form of near sets that model human perception in a physical continuum. This is a humanistic perception-based near set approach, where tolerances become part of the solution to the image correspondence problem. Near sets are a generalization of rough sets introduced by Zdzisław Pawlak during the early 1980s. The basic idea in devising near set-based measures of resemblance of images that emulate human perception is to allow overlapping classes in image coverings defined with respect to a tolerance ε. The contribution of this article is the introduction of two new tolerance class-based image resemblance measures and a comparison of the new measures with the original Henry-Peters image nearness measure.
Intelligent Analysis of Images & Videos (IAI& V 2009). The insights and suggestions by Homa Fashandi, Christopher Henry, Leszek Puzio, Andrzej Skowron and Piotr Wasilewski concerning topics in this article are gratefully acknowledged. This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grants 185986 & 194376, Canadian Arthritis Network grant SRI-BIO-05, and a grant from Manitoba Hydro T277.
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References
Henry, C., Peters, J.F.: Image pattern recognition using approximation spaces and near sets. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 475–482. Springer, Heidelberg (2007)
Henry, C., Peters, J.F.: Near set index in an objective image segmentation evaluation framework. In: GEOgraphic Object Based Image Analysis: Pixels, Objects, Intelligence, University of Calgary, Alberta, pp. 1–6 (2008)
Peters, J.F., Wasilewski, P.: Foundations of near sets. Information Sciences 179(18), 3091–3109 (2009)
Henry, C., Peters, J.: Perception-based image analysis. Int. J. of Bio-Inspired Computation 2(2) (to appear, 2009)
Pawlak, Z., Peters, J.: Jak blisko (how near). Systemy Wspomagania Decyzji I, 57–109 (2002)
Peters, J.: Near sets. special theory about nearness of objects. Fundamenta Informaticae 76, 1–27 (2007)
Peters, J.F.: Near sets. general theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2629 (2007)
Peters, J.F.: Tolerance near sets and image correspondence. Int. J. of Bio-Inspired Computation 4(1), 239–245 (2009)
Orłowska, E.: Semantics of vague concepts. applications of rough sets. Technical Report 469, Institute for Computer Science, Polish Academy of Sciences (1982)
Orłowska, E.: Semantics of vague concepts. In: Dorn, G., Weingartner, P. (eds.) Foundations of Logic and Linguistics. Problems and Solutions, pp. 465–482. Plenum Pres, London (1985)
Zeeman, E.C.: The topology of the brain and the visual perception. Prentice Hall, New Jersey (1965); Fort, K.M. (ed.): Topology of 3-manifolds and Selected Topics, pp. 240–256
Sossinsky, A.B.: Tolerance space theory and some applications. Acta Applicandae Mathematicae: An International Survey Journal on Applying Mathematics and Mathematical Applications 5(2), 137–167 (1986)
Poincaré, H.: The topology of the brain and the visual perception. Prentice Hall, New Jersey (1965); Fort, K.M. (ed.): Topology of 3-manifolds and Selected Topics, pp. 240–256
Hassanien, A.E., Abraham, A., Peters, J.F., Schaefer, G., Henry, C.: Rough sets and near sets in medical imaging: A review. IEEE TRansactions on Information Technology in Biomedicine (to appear, 2009)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Peters, J.F.: Discovery of perceptually near information granules. In: Yao, J.T. (ed.) Novel Developements in Granular Computing: Applications of Advanced Human Reasoning and Soft Computation. Information Science Reference, Hersey, N.Y., USA (in press, 2009)
Peters, J.F., Ramanna, S.: Affinities between perceptual granules: Foundations and perspectives. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling. SCI, vol. 182, pp. 49–66. Springer, Heidelberg (2009)
Bartol, W., Miró, J., Pióro, K., Rosselló, F.: On the coverings by tolerance classes. Inf. Sci. Inf. Comput. Sci. 166(1-4), 193–211 (2004)
Gerasin, S.N., Shlyakhov, V.V., Yakovlev, S.V.: Set coverings and tolerance relations. Cybernetics and Sys. Anal. 44(3), 333–340 (2008)
Schroeder, M., Wright, M.: Tolerance and weak tolerance relations. Journal of Combinatorial Mathematics and Combinatorial Computing 11, 123–160 (1992)
Shreider, Y.A.: Tolerance spaces. Cybernetics and Systems Analysis 6(12), 153–758 (1970)
Skowron, A., Stepaniuk, J.: Tolerance Approximation Spaces. Fundamenta Informaticae 27(2/3), 245–253 (1996)
Zheng, Z., Hu, H., Shi, Z.: Tolerance Relation Based Granular Space. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, p. 682. Springer, Heidelberg (2005)
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Meghdadi, A.H., Peters, J.F., Ramanna, S. (2009). Tolerance Classes in Measuring Image Resemblance. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_16
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