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Proximity System: A Description-Based System for Quantifying the Nearness or Apartness of Visual Rough Sets

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Transactions on Rough Sets XVII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8375))

Abstract

This article introduces the Proximity System, an application developed to demonstrate descriptive-based approaches to nearness and proximity within the context of digital image analysis. Specifically, the system implements the descriptive-based intersection, compliment, and difference operations defined on sets of pixels representing regions of interest. These sets of pixels can be considered visual rough sets, since the results of the descriptive-based operators are always defined with respect to a set of probe functions, which induce a partition of the objects (pixels) being considered. The contribution of this article is an overview of the Proximity System, its use of visual rough sets as description-based operands, its ability to quantify the nearness or apartness of visual rough sets, and a practical application to the problem of human visual search.

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant 418413.

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References

  1. The Corel Stock Photo Library (PC/MAC ver.). Corel Corporation, Ottawa, Ontario, Canada (1994)

    Google Scholar 

  2. Benjamin Jr., L.T.: A Brief History of Modern Psychology. Blackwell Publishing, Malden (2007)

    Google Scholar 

  3. Borkowski, M.: 2D to 3D Conversion with Direct Geometrical Search and Approximation Spaces. Ph.D. thesis, University of Manitoba (2007)

    Google Scholar 

  4. Borkowski, M., Peters, J.F.: Matching 2D image segments with genetic algorithms and approximation spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 63–101. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  6. Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annual Review of Neuroscience 18, 193–222 (1995)

    Article  Google Scholar 

  7. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley (2001)

    Google Scholar 

  8. Duncan, J.: Converging levels of analysis in the cognitive neuroscience of visual attention. Philosophical Transactions: Biological Sciences 353(1373), 1307–1317 (1998)

    Article  Google Scholar 

  9. Duncan, J., Humphreys, G., Ward, R.: Competitive brain activity in visual attention. Current Opinion in Neurobiology 7(2), 255–261 (1997)

    Article  Google Scholar 

  10. Fang, Y., Zhen, Z., Huang, Z., Zhang, C.: Multi-objective fuzzy clustering method for image segmentation based on variable-length intelligent optimization algorithm. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 329–337. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Haralick, R.M.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973)

    Article  MathSciNet  Google Scholar 

  12. Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  13. 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 13(6), 955–968 (2009)

    Article  Google Scholar 

  14. Henry, C.: Near set Evaluation And Recognition (NEAR) system. In: Pal, S.K., Peters, J.F. (eds.) Rough Fuzzy Analysis Foundations and Applications, pp. 7–1 – 7–22. CRC Press, Taylor & Francis Group (2010), http://wren.ee.umanitoba.ca

  15. Henry, C.J.: Near Sets: Theory and Applications. Ph.D. thesis, University of Manitoba, CAN (2010), https://mspace.lib.umanitoba.ca/handle/1993/4267

  16. Henry, C.J.: Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XV. LNCS, vol. 7255, pp. 41–121. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Henry, C.J.: Metric free nearness measure using description-based neighbourhoods. Mathematics in Computer Science 7(1), 51–69 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  18. Henry, C.J., Peters, J.F., Hettiarchachichi, R., Ramanna, S.: Content-based image retrieval using a metric free nearness measure. In: Proceedings of the 15th IASTED International Conference on Signal and Image Processing, pp. 374–381 (2013)

    Google Scholar 

  19. Henry, C.J., Ramanna, S.: Maximal clique enumeration in finding near neighbourhoods. In: Peters, J.F., Skowron, A., Ramanna, S., Suraj, Z., Wang, X. (eds.) Transactions on Rough Sets XVI. LNCS, vol. 7736, pp. 103–124. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  20. Henry, C.J., Ramanna, S.: Signature-based perceptual nearness. Application of near sets to image retrieval. Mathematics in Computer Science 7(1), 71–85 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  21. Henry, C.J., Ramanna, S., Levy, D.: Quantifying nearness in visual spaces. Cybernetics and Systems 44(1), 38–56 (2013)

    Article  Google Scholar 

  22. Henry, C.J., Smith, G.: Proximity system. Tech. rep., Computational Intelligence Laboratory, University of Manitoba (2012), uM CI Laboratory Technical Report No. TR-2012-021

    Google Scholar 

  23. Hergenhahn, B.R.: An Introduction to the History of Psychology. Wadsworth Publishing, Belmont (2009)

    Google Scholar 

  24. Maji, P., Pal, S.K.: Maximum class separability for rough-fuzzy C-means based brain MR image segmentation. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 114–134. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, California (1999)

    MATH  Google Scholar 

  26. Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(7), 710–732 (1992)

    Article  Google Scholar 

  27. Małyszko, D., Stepaniuk, J.: Standard and fuzzy rough entropy clustering algorithms in image segmentation. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 409–418. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  28. Małyszko, D., Stepaniuk, J.: Fuzzified probabilistic rough measures in image segmentation. In: Kim, T.-h., Pal, S.K., Grosky, W.I., Pissinou, N., Shih, T.K., Ślęzak, D. (eds.) SIP/MulGraB 2010. CCIS, vol. 123, pp. 78–86. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  29. Małyszko, D., Stepaniuk, J.: Probabilistic rough entropy measures in image segmentation. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 40–49. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  30. Małyszko, D., Stepaniuk, J.: Rough fuzzy measures in image segmentation and analysis. In: Pal, S.K., Peters, J.F. (eds.) Rough Fuzzy Analysis Foundations and Applications, pp. 11-1–11-25. CRC Press, Taylor & Francis Group (2010) ISBN 13: 9781439803295

    Google Scholar 

  31. Małyszko, D., Stepaniuk, J.: Rough entropy hierarchical agglomerative clustering in image segmentation. In: Peters, J.F., Skowron, A., Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 89–103. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  32. Mrózek, A., Mrózek, L.: Rough sets in image analysis. Foundations of Computing and Decision Sciences F18(3-4), 268–273 (1993)

    Google Scholar 

  33. Mushrif, M., Ray, A.K.: Color image segmentation: Rough-set theoretic approach. Pattern Recognition Letters 29(4), 483–493 (2008)

    Article  Google Scholar 

  34. Naimpally, S.A.: Near and far. A centennial tribute to Frigyes Riesz. Siberian Electronic Mathematical Reports 6, A.1–A.10 (2009)

    Google Scholar 

  35. Naimpally, S.A.: Proximity Approach to Problems in Topology and Analysis. Oldenburg Verlag, München (2009) ISBN 978-3-486-58917-7

    Google Scholar 

  36. Naimpally, S.A., Peters, J.F.: Topology with Applications.Topological Spaces via Near and Far. World Scientific, Singapore (2013)

    Book  MATH  Google Scholar 

  37. Naimpally, S.A., Warrack, B.D.: Proximity spaces. Cambridge Tract in Mathematics, vol. 59. Cambridge University Press, Cambridge (1970)

    MATH  Google Scholar 

  38. Orłowska, E.: Semantics of vague concepts. Applications of rough sets. Tech. Rep. 469, Institute for Computer Science, Polish Academy of Sciences (1982)

    Google Scholar 

  39. 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/NY (1985)

    Chapter  Google Scholar 

  40. Pal, S.K., Mitra, P.: Multispectral image segmentation using rough set initialized em algorithm. IEEE Transactions on Geoscience and Remote Sensing 11, 2495–2501 (2002)

    Article  Google Scholar 

  41. Pal, S.K., Peters, J.F.: Rough Fuzzy Image Analysis: Foundations and Methodologies. CRC Press, Boca Raton (2010)

    Google Scholar 

  42. Pal, S.K., Shankar, B.U., Mitra, P.: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16), 401–416 (2005)

    Article  Google Scholar 

  43. Pavel, M.: Fundamentals of Pattern Recognition. Marcel Dekker, Inc., NY (1993)

    MATH  Google Scholar 

  44. Pawlak, Z.: Classification of objects by means of attributes. Tech. Rep. PAS 429, Institute for Computer Science, Polish Academy of Sciences (1981)

    Google Scholar 

  45. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  46. Pawlak, Z., Peters, J.F.: Jak blisko (how near). Systemy Wspomagania Decyzji I, 57–109 (2002)

    Google Scholar 

  47. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  48. Peters, J.F.: Classification of objects by means of features. In: Proceedings of the IEEE Symposium Series on Foundations of Computational Intelligence (IEEE SCCI 2007), pp. 1–8 (2007)

    Google Scholar 

  49. Peters, J.F.: Near sets. General theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2629 (2007)

    MATH  MathSciNet  Google Scholar 

  50. Peters, J.F.: Near sets. Special theory about nearness of objects. Fundamenta Informaticae 75(1-4), 407–433 (2007)

    MATH  MathSciNet  Google Scholar 

  51. Peters, J.F.: Classification of perceptual objects by means of features. International Journal of Information Technology & Intelligent Computing 3(2), 1–35 (2008)

    Google Scholar 

  52. Peters, J.F.: Tolerance near sets and image correspondence. International Journal of Bio-Inspired Computation 1(4), 239–245 (2009)

    Article  Google Scholar 

  53. Peters, J.F.: Corrigenda and addenda: Tolerance near sets and image correspondence. International Journal of Bio-Inspired Computation 2(5), 310–318 (2010)

    Article  Google Scholar 

  54. Peters, J.F.: How near are Zdzisław Pawlak’s? In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, pp. 545–568. Springer, Berlin (2013)

    Google Scholar 

  55. Peters, J.F.: Local near sets. pattern discovery in proximity spaces. Mathematics in Computer Science 7(1), 87–106 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  56. Peters, J.F.: Near sets: An introduction. Mathematics in Computer Science 7(1), 3–9 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  57. Peters, J.F., Borkowski, M.: K-means indiscernibility relation over pixels. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 580–585. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  58. Peters, J.F., Naimpally, S.A.: Applications of near sets. Notices of the American Mathematical Society 59(4), 536–542 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  59. 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, pp. 49–66. Springer, Berlin (2009)

    Chapter  Google Scholar 

  60. Peters, J.F., Wasilewski, P.: Foundations of near sets. Info. Sci. 179(18), 3091–3109 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  61. Peters, J.F., Wasilewski, P.: Tolerance spaces: Origins, theoretical aspects and applications. Information Sciences 195, 211–225 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  62. Poincaré, H.: Science and Hypothesis. The Mead Project, Brock University (1905), L. G. Ward’s translation

    Google Scholar 

  63. Polkowski, L.: Rough Sets. Mathematical Foundations. Springer, Heidelberg (2002)

    Book  MATH  Google Scholar 

  64. Ramanna, S., Peters, J.F.: Nearness in associated rough sets: Case study in image analysis. In: Peters, G., Lingras, P., Slezak, D., Yao, Y. (eds.) Selected Methods and Applications of Rough Sets in Management and Engineering, pp. 62–73. Springer, Berlin (2011)

    Google Scholar 

  65. Sen, D., Pal, S.K.: Generalized rough sets, entropy, and image ambiguity measures. IEEE Transactions on Systems, Man, and Cybernetics - Part B 39(1), 117–128 (2009)

    Article  Google Scholar 

  66. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  67. 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)

    Article  MathSciNet  Google Scholar 

  68. Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)

    Article  Google Scholar 

  69. Wolfe, J.M.: Guided search 2.0 a revised model of visual search. Psychonomic Bulletin & Review 1(2), 202–238 (1994)

    Article  Google Scholar 

  70. Yu, Y., Mann, G.K.I., Gosine, R.G.: A goal-directed visual perception system using object-based top-down attention. IEEE Transactions on Autonomous Mental Development 4(1), 87–103 (2012)

    Article  Google Scholar 

  71. Zeeman, E.C.: The topology of the brain and the visual perception. In: Fort, K.M. (ed.) Topoloy of 3-manifolds and selected topices, pp. 240–256. Prentice Hall, New Jersey (1965)

    Google Scholar 

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Henry, C.J., Smith, G. (2014). Proximity System: A Description-Based System for Quantifying the Nearness or Apartness of Visual Rough Sets. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XVII. Lecture Notes in Computer Science, vol 8375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54756-0_3

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