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Scribble-based object segmentation with modified gaussian mixture models

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Abstract

In this paper, we present an interactive segmentation method, designed to help the user to extract an object of interest from an image. The proposed approach adopts the scribble-based segmentation paradigm. The user interaction consists of specifying a set of lines, corresponding to both foreground and background scribbles. The segmentation process is based on color distributions, estimated with Gaussian mixture models (GMM). We show that such a technique presents some limitations when dealing with compressed images, even for relatively high quality compression factors: in this case, blocking artifacts may degrade the segmentation results. In order to overcome such a drawback, a modified GMM model, which re-shapes the Gaussian mixture based on the eigenvalues of the GMM components, is proposed. The experimental evaluation, carried out on a corpus of various images with different characteristics and textures, demonstrates the superiority of the modified GMM model which is able to appropriately take into account compression artifacts.

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References

  1. Protiere A, Sapiro G (2007) Interactive image segmentation via adaptive weighted distances. IEEE Trans Image Process 16(4):1046–1057

    Article  MathSciNet  Google Scholar 

  2. Bai X, Sapiro G (2007) A geodesic framework for fast interactive image and video segmentation and matting, IEEE 11th International Conference on Computer Vision, pp 1–8

  3. Boykov Y, Jolly M-P (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. International Conference on Computer Vision (ICCV) 1:105–112

    Google Scholar 

  4. Gulshan V, Rother C, Criminisi A, Blake A, Zisserman A (2010) Geodesic star convexity for interactive image segmentation. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3129–3136

  5. Blake A, Rother C, Brown M, Perez P, Torr P (2004) Interactive image segmentation using an adaptive GMMRF model. In:Proceedings of Computer Vision—ECCV 2004, Vol 3021, pp 428–441

  6. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  7. Veksler O (2008) Star shape prior for graph-cut image segmentation. In: Proceedings of the 10th European Conference on Computer Vision, Part III, pp 454–467

  8. Kyrki SV, Kamarainen JK (2004) Simple Gabor feature space for invariant object recognition. Pattern Recogn Lett 25(3):311–318

    Article  Google Scholar 

  9. Tkalcic M, Tasic JF (2003) Color spaces: perceptual, historical and application background”. In: Proceedings of IEEE EUROCON, Vol 1, pp 304–308, Sep. 2003

  10. Yang C, Duraiswami R, Gumerov N, Davis L (2003) Improved fast Gauss transform and efficient kernel density estimation. Ninth IEEE International Conference on Computer Vision, pp 664–671

  11. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

    Article  MATH  Google Scholar 

  12. Reynolds D (2007) Gaussian mixture models, Encyclopedia of Biometric Recognition

  13. Smith C (1968) A characterization of star-shaped sets. Am Math Mon 75(4):386

    Article  MathSciNet  MATH  Google Scholar 

  14. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc, Series B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  15. Moon Todd K (1996) The expectation-maximation algorithm. IEEE Signal Process Mag 13(6):47–70

    Article  Google Scholar 

  16. Rissanen J (1983) A universal prior for integers and estimation by minimum description length. Annal Stat, Colume 11(2):416–431

    Article  MathSciNet  MATH  Google Scholar 

  17. Bouman CA, Shapiro M, Cook GW, Atkins CB, Cheng H (1997) Cluster: an unsupervised algorithm for modelling gaussian mixtures. School of Electrical Engineering, Purdue University. http://dynamo.ecn.purdue.edu/~bouman/

  18. GrabCut image dataset http://research.microsoft.com/en-us/um/cambridge/projects/visionimagevideoediting/segmentation/grabcut.htm

  19. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2009) The pascal visual object classes challenge, (VOC2009) Results

  20. Rhemann C, Rother C, Wang J, Gelautz M, Kohli P, Rott P (2009) A perceptually motivated online benchmark for image matting, In: Proceedigs of CVPR, pp 1826–1833

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Authors and Affiliations

  1. ARTEMIS Departement, Institut Mines-Télécom, TELECOM SudParis, UMR CNRS 8145-MAP5, 9 Rue Charles Fourier, Evry, France

    Raluca-Diana Şambra-Petre & Titus Zaharia

  2. Alcatel-Lucent Bell Labs France, Route de Villejust, Nozay, France

    Raluca-Diana Şambra-Petre

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  1. Raluca-Diana Şambra-Petre
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  2. Titus Zaharia
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Correspondence to Raluca-Diana Şambra-Petre.

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Şambra-Petre, RD., Zaharia, T. Scribble-based object segmentation with modified gaussian mixture models. Pattern Anal Applic 19, 593–609 (2016). https://doi.org/10.1007/s10044-014-0406-6

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  • DOI: https://doi.org/10.1007/s10044-014-0406-6

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