Skip to main content

Descriptive Topological Spaces for Performing Visual Search

  • Chapter
  • First Online:
Transactions on Rough Sets XXI

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 10810))

  • 285 Accesses

Abstract

This article presents an approach to performing the task of visual search in the context of descriptive topological spaces. The presented algorithm forms the basis of a descriptive visual search system (DVSS) that is based on the guided search model (GSM) that is motivated by human visual search. This model, in turn, consists of the bottom-up and top-down attention models and is implemented within the DVSS in three distinct stages. First, the bottom-up activation process is used to generate saliency maps and to identify salient objects. Second, perceptual objects, defined in the context of descriptive topological spaces, are identified and associated with feature vectors obtained from a VGG deep learning convolutional neural network. Lastly, the top-down activation process makes decisions on whether the object of interest is present in a given image through the use of descriptive patterns within the context of a descriptive topological space. The presented approach is tested with images from the ImageNet ILSVRC2012 and SIMPLIcity datasets. The contribution of this article is a descriptive pattern-based visual search algorithm.

Jiajie Yu—This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 418413, and the Faculty of Graduate Studies at the University of Winnipeg. Also, special thanks to Keith Massey for developing the code that produced the VGG object descriptions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The term perceptual object has specific meaning in descriptive set theory and perceptual systems. Hence, we will use visual object to represent any salient object in an FOV.

References

  1. Yu, Y., Mann, G.K.I., Gosine, R.G.: A goal-directed visual perception system using object-based top-down attention. IEEE Trans. Auton. Ment. Dev. 4(1), 87–103 (2012)

    Article  Google Scholar 

  2. Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychol. Rev. 96(3), 433 (1989)

    Article  Google Scholar 

  3. Wolfe, J.M.: Guided search 2.0 a revised model of visual search. Psychon. Bull. Rev. 1, 202–238 (1994)

    Article  Google Scholar 

  4. Peters, J.F., Naimpally, S.A.: Applications of near sets. Not. Am. Math. Soc. 59(4), 536–542 (2012)

    MathSciNet  MATH  Google Scholar 

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

    Book  Google Scholar 

  6. Peters, J.F.: Topology of Digital Images. Visual Pattern Discovery in Proximity Spaces. Intelligent Systems Reference Library, vol. 63. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-53845-2

    Book  MATH  Google Scholar 

  7. Peters, J.: Computational Proximity: Excursions in the Topology of Digital Images. Intelligent Systems Reference Library. Springer, Berlin (2016). https://doi.org/10.1007/978-3-319-30262-1

    Book  MATH  Google Scholar 

  8. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2006)

    Google Scholar 

  9. Yu, J.: A descriptive topological framework for performing visual search, Masters thesis, University of Winnipeg (2017)

    Google Scholar 

  10. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  11. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)

    Article  Google Scholar 

  12. Dismone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995)

    Article  Google Scholar 

  13. Duncan, J., Humphreys, G., Ward, R.: Competitive brain activity in visual attention. Curr. Opin. Neurobiol. 7(2), 255–261 (1997)

    Article  Google Scholar 

  14. Fink, G.R., Dolan, R.J., Halligan, P.W., Marshall, J.C., Frith, C.D.: Space-base and object-based visual attention: shared and specific neural domains. Brain 120(11), 2013–2028 (1997)

    Article  Google Scholar 

  15. Peters, J.F.: Near sets. In: Henry, C.J. (ed.) Wikipedia, The Free Encyclopaedia (2015)

    Google Scholar 

  16. Sossinsky, A.B.: Tolerance space theory and some applications. Acta Applicandae Mathematicae: Int. Surv. J. Appl. Math. Math. Appl. 5(2), 137–167 (1986)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  21. Naimpally, S.A.: Near and far. A centennial tribute to Frigyes Riesz. Siberian Electron. Math. Rep. 6, A.1–A.10 (2009)

    MathSciNet  MATH  Google Scholar 

  22. Naimpally, S.A., Warrack, B.D.: Proximity spaces. In: Cambridge Tract in Mathematics No. 59. Cambridge University Press, Cambridge (1970)

    Google Scholar 

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

    Google Scholar 

  24. Mozzochi, C.J., Naimpally, S.A.: Uniformity and proximity. In: Allahabad Mathematical Society Lecture Note Series, vol. 2, p. 153 pp. The Allahabad Mathematical Society, Allahabad (2009)

    Google Scholar 

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

    Book  Google Scholar 

  26. Hocking, J.G., Naimpally, S.A.: Nearness-a better approach to continuity and limits. In: Allahabad Mathematical Society Lecture Note Series, vol. 3, p. 153 pp. The Allahabad Mathematical Society (2009)

    Google Scholar 

  27. Peters, J.F.: Near sets. General theory about nearness of objects. Appl. Math. Sci. 1(53), 2609–2629 (2007)

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  29. Peters, J.F.: Tolerance near sets and image correspondence. Int. J. Bio-Inspired Comput. 1(4), 239–245 (2009)

    Article  Google Scholar 

  30. Peters, J.F.: Corrigenda and addenda: tolerance near sets and image correspondence. Int. J. Bio-Inspired Comput. 2(5), 310–318 (2010)

    Article  Google Scholar 

  31. İnan, E., Öztürk, M.A.: Near groups on nearness approximation spaces. Hacettepe J. Math. Stat. 41(4), 545–558 (2012)

    MathSciNet  MATH  Google Scholar 

  32. Peters, J.F., İnan, E., Öztürk, M.A.: Spatial and descriptive isometries in proximity spaces. Gen. Math. Notes 21(2), 1–10 (2014)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  34. Peters, J.F.: Classification of perceptual objects by means of features. Int. J. Inf. Technol. Intell. Comput. 3(2), 1–35 (2008)

    Google Scholar 

  35. Li, F., Karpathy, A.: CS231n: Convolutional Neural Networks for Visual Recognition, Course Lecture Notes, Standford University (2015)

    Google Scholar 

  36. Henry, C.J., Smith, G.: 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. LNCS, vol. 8375, pp. 48–73. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54756-0_3

    Chapter  Google Scholar 

  37. Henry, C.J.: Near sets: theory and applications, Ph.D. thesis, University of Manitoba (2010)

    Google Scholar 

  38. Henry, C.J.: Metric free nearness measure using description-based neighbourhoods. Math. Comput. Sci. 7(1), 51–69 (2013)

    Article  MathSciNet  Google Scholar 

  39. Peters, J.F.: Computational proximity. In: Computational Proximity. ISRL, vol. 102, pp. 1–62. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30262-1_1

    Google Scholar 

  40. C̆ech, E.: Topological Spaces. Wiley, London (2014). fr seminar, Brno, 1936–1939; rev. ed. Z. Frolik, M. Katĕtov

    Google Scholar 

  41. Efremovic̆, V.A.: The geometry of proximity I (in Russian). Mat. Sb. (N.S.) 31(73)(1), 189–200 (1952)

    MathSciNet  Google Scholar 

  42. Lodato, M.: On topologically induced generalized proximity relations, Ph.D. dissertation, Rutgers University (1962). supervisor: S. Leader

    Google Scholar 

  43. Wallman, H.: Lattices and topological spaces. Ann. Math. 39(1), 112–126 (1938)

    Article  MathSciNet  Google Scholar 

  44. Peters, J.F.: Local near sets. Pattern discovery in proximity spaces. Math. Comput. Sci. 7(1), 87–106 (2013)

    Article  MathSciNet  Google Scholar 

  45. Peters, J.F., Wasilewski, P.: Tolerance spaces: origins, theoretical aspects and applications. Inf. Sci. 195, 211–225 (2012)

    Article  MathSciNet  Google Scholar 

  46. 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). https://doi.org/10.1007/978-3-642-31903-7_3

    Chapter  Google Scholar 

  47. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR, pp. 1–14 (2015). http://arxiv.org/abs/1409.1556

  48. Karpathy, A.: CS231n: Convolutional Neural Networks for Visual Recognition. Stanford University, Stanford (2015)

    Google Scholar 

  49. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1–9 (2012)

    Google Scholar 

  50. Perone, C.: Deep learning - convolutional neural networks and feature extraction with python (2015). http://blog.christianperone.com/2015/08/convolutional-neural-networks-and-feature-extraction-with-python//

  51. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  52. Yates-Baeza, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press/Pearson Addison Wesley, New York (1999)

    Google Scholar 

  53. Deng, J.D.J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2–9 (2009)

    Google Scholar 

  54. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision (IJCV) 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  55. Wang, J.Z., Li, J., Wiederholdy, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 360–371. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-40053-2_32

    Chapter  Google Scholar 

  56. Jhanwar, N., Chaudhuri, S., Seetharaman, G., Zavidovique, B.: Content based image retrieval using motif cooccurrence matrix. Image Vis. Comput. 22(14), 1211–1220 (2004)

    Article  Google Scholar 

  57. Subrahmanyam, M., Jonathan Wu, Q.M., Maheshwari, R.P., Balasubramanian, R.: Modified color motif co-occurrence matrix for image indexing and retrieval. Comput. Electr. Eng. 39(3), 762–774 (2013)

    Article  Google Scholar 

  58. Vadivel, A., Sural, S., Majumdar, A.K.: An integrated color and intensity co-occurrence matrix. Pattern Recogn. Lett. 28(8), 974–983 (2007)

    Article  Google Scholar 

  59. Lin, C.-H., Chen, R.-T., Chan, Y.-K.: A smart content-based image retrieval system based on color and texture feature. Image Vis. Comput. 27(6), 658–665 (2009). https://doi.org/10.1016/j.imavis.2008.07.004

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher J. Henry .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yu, J., Henry, C.J. (2019). Descriptive Topological Spaces for Performing Visual Search. In: Peters, J., Skowron, A. (eds) Transactions on Rough Sets XXI. Lecture Notes in Computer Science(), vol 10810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58768-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-58768-3_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-58767-6

  • Online ISBN: 978-3-662-58768-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics