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A Probabilistic Interpretation of the Saliency Network

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)

Abstract

The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to find image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modified saliency estimation mechanism, which is based on probabilistically specified grouping cues and on length estimation. In the context of the proposed method, the wellknown saliency mechanism, proposed by Shaashua and Ullman [SU88], may be interpreted as a process trying to detect the curve with maximal expected length.

The new characterization of saliency using probabilistic cues is conceptually built on considering the curve starting at a feature point, and estimating the distribution of the length of this curve, iteratively. Different saliencies, like the expected length, may be specified as different functions of this distribution. There is no need however to actually propagate the distributions during the iterative process.

The proposed saliency characterization is associated with several advantages: First, unlike previous approaches, the search for the “best group” is based on a probabilistic characterization, which may be derived and verified from typical images, rather than on pre-conceived opinion about the nature of figure subsets. Therefore, it is expected also to be more reliable. Second, the probabilistic saliency is more abstract and thus more generic than the common geometric formulations. Therefore, it lends itself to different realizations of saliencies based on different cues, in a systematic rigorous way. To demonstrate that, we created, as instances of the general approach, a saliency process which is based on grey level similarity but still preserve a similar meaning. Finally, the proposed approach gives another interpretation for the measure than makes one curve a winner, which may often be more intuitive to grasp, especially as the saliency levels has a clear meaning of say, expected curve length.

Keywords

  • Feature Point
  • Length Distribution
  • Human Visual System
  • Perceptual Organization
  • Probabilistic Interpretation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. T.D. Alter and R. Basri. Extracting salient curves from images: An analysis of the saliency network. IJCV, 27(1):51–69, March 1998.

    CrossRef  Google Scholar 

  2. A. Amir and M. Lindenbaum. Ground from figure discrimination. In CVPR98, pages 521–527, 1998.

    Google Scholar 

  3. Laurent Alquier and Philippe Montesinos. Representation of linear structures using perceptual grouping. In Presented in The 1st workshop on Perceptual Organization in Computer Vision, 1998.

    Google Scholar 

  4. T.H. Cormen, C.E. Leiserson, and R.L. Rivest. Introduction to Algorithms. MIT Press, 1990.

    Google Scholar 

  5. G. Guy and G.G. Medioni. Inferring global perceptual contours from local features. In CVPR93, pages 787–787, 1993.

    Google Scholar 

  6. Laurent Herault and Radu Horaud. Figure-ground discrimination: A combinatorial optimization approach. PAMI, 15(9):899–914, Sep 1993.

    CrossRef  Google Scholar 

  7. Friedrich Heitger and Rudiger von der Heydt. A computational model of neural contour processing: Figure-ground segregation and illusory contours. In ICCV-93, Berlin, pages 32–40, 1993.

    Google Scholar 

  8. David G. Lowe. Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, 1985.

    Google Scholar 

  9. D. Marr. Vision: A computational investigation into the human representation and processing of visual information. In W.H. Freeman, 1982.

    Google Scholar 

  10. S. Sarkar and K.L. Boyer. Quantitative measures of change based on feature organization: Eigenvalues and eigenvectors. CVIU, 71(1):110–136, July 1998.

    Google Scholar 

  11. Amnon Sha’ashua and Shimon Ullman. Structural saliency: The detection of globally salient structures using locally connected network. In ICCV-88, pages 321–327, 1988.

    Google Scholar 

  12. Max Wertheimer. Laws of organization in perceptual forms. In Willis D. Ellis, editor, A Source Book of Gestalt Psychology, pages 71–88, 1950.

    Google Scholar 

  13. L.R. Williams and D.W. Jacobs. Local parallel computation of stochastic completion fields. In CVPR96, pages 161–168, 1996.

    Google Scholar 

  14. L. Williams and K. Thornber. A comparison of measures for detecting natural shapes in cluttered backgrounds. In ECCV98, 1998.

    Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Lindenbaum, M., Berengolts, A. (2000). A Probabilistic Interpretation of the Saliency Network. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_17

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  • DOI: https://doi.org/10.1007/3-540-45053-X_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

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