Skip to main content
Springer Nature Link
Log in

Unsupervised colour image segmentation by low-level perceptual grouping

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Note that each measure has different criteria and these criteria are particularly important for understanding the graphical results offered in Sect. 3.3.

  2. The same scale for y-axes has been applied in both plots.

  3. Image results for GSEG algorithm were provided by the author for all the BSD and they offer an average time of 24 s per image in their paper. Likewise, our implementation and the ones for FH, MS and JSEG algorithms have been programmed in C language, however, the SRM algorithm were provided in MATLAB by the authors.

References

  1. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recognit 26(9):1277–1294

    Article  Google Scholar 

  2. Zucker S (1976) Region growing: childhood and adolescence. CGIP 5:382–399

    Google Scholar 

  3. Fu K, Mui J (1981) A survey on image segmentation. Pattern Recognit 13:3–16

    Article  MathSciNet  Google Scholar 

  4. Lucchese L, Mitra S (1999) Advances in color image segmentation. GLOBECOM 4:2038–2044

    Google Scholar 

  5. Haralick RH, Shapiro LG (1985) Image segmentation techniques. CVGIP 29:100–132

    Google Scholar 

  6. Cheng H-D, Jiang XH, Sun Y, Wang J (2001) Color image segmentation: advances and prospects. Pattern Recognit 34(12):2259–2281

    Article  MATH  Google Scholar 

  7. Sahoo PK, Soltani S, Wong AK, Chen YC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260

    Article  Google Scholar 

  8. Tabb M, Ahuja N (1997) Multiscale image segmentation by integrated edge and region detection. IEEE Trans Image Process 6(5):642–655

    Article  Google Scholar 

  9. Todorovic S, Ahuja N (2008) Unsupervised category modeling, recognition and segmentation in images. IEEE Trans PAMI 30(12):2158–2174

    Article  Google Scholar 

  10. Beveridge J, Griffith JS, Kohler RR, Hanson A, Riseman E (1989) Segmenting images using localized histograms and region merging. Int J Comput Vis 2(3):311–352

    Article  Google Scholar 

  11. Rubner Y, Puzicha J, Tomasi C, Buhmann JM (2001) Empirical evaluation of dissimilarity measures for color and texture. CVIU 84(1):25–43

    MATH  Google Scholar 

  12. Todorovic S, Ahuja N (2006) Extracting subimages of an unknown category from a set of images. CVPR 927–934

  13. Tremeau A, Borel N (1997) A region growing and merging algorithm to color segmentation. Pattern Recognit 30(7):1191–1203

    Article  Google Scholar 

  14. Pauwels EJ, Frederix G (1999) Finding salient regions in images: non-parametric clustering for image segmentation and grouping. CVIU 75(1/2):73–85

    Google Scholar 

  15. Randall J, Guan L, Li W, Zhang X (2008) The HCM for perceptual image segmentation. Neurocomputing 71(10–12):1966–1979

    Article  Google Scholar 

  16. Haxhimusa Y, Kropatsch WG (2004) Segmentation graph hierarchies. In: Proceedings of the SSPR-SPR, pp 343–351

  17. Mirmehdi M, Petrou M (2000) Segmentation of color textures. IEEE Trans PAMI 22(2):142–159

    Article  Google Scholar 

  18. Chen J, Pappas TN, Mojsilović A, Rogowitz BE (2005) Adaptive perceptual color-texture image segmentation. IEEE Trans Image Process 14(10):1524–1536

    Article  Google Scholar 

  19. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans PAMI 22(8):888–905

    Article  Google Scholar 

  20. Gdalyahu Y, Weinshall D, Werman M (2001) Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans PAMI 23:10531074.

    Google Scholar 

  21. Paschos G (2001) Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Trans Image Process 10(6):932–937

    Article  MATH  Google Scholar 

  22. Alata O, Quintard L (2009) Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model? Comput Vis Image Underst 113 (8):867–877

    Article  Google Scholar 

  23. Zhang H, Goldman SA (2006) Perceptual information of images and the bias in homogeneity-based segmentation. In: Proceedings of the CVPR, pp 181–188

  24. Palmer S, Rock I (1994) Rethinking perceptual organization: the role of uniform connectedness. Psychonom Bull Rev 1(1):29–55

    Google Scholar 

  25. Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern Recognit 29(8):1335–1346

    Article  Google Scholar 

  26. Cardoso JS, Corte-Real L (2005) Toward a generic evaluation of image segmentation. IEEE Trans Image Process 14(11):1773–1782

    Article  Google Scholar 

  27. Chabrier S, Emile B, Laurent H, Rosenberger C, Marché P (2004) Unsupervised evaluation of image segmentation application to multi-spectral images. ICPR 1:576–579

    Google Scholar 

  28. Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 110(2):260–280

    Article  Google Scholar 

  29. Deng Y, Kenney C, Moore MS, Manjunath BS (1999) Peer group filtering and perceptual color image quantization. In: Proceedings of the IEEE ISCS, vol 4, pp 21–24

  30. Schettini R (1993) A segmentation algorithm for color images. Pattern Recognit Lett 14(6):499–506

    Article  Google Scholar 

  31. Weiss Y, Adelson EH (1996) A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models. CVPR 321–326

  32. Mansouri A-R, Mitiche A, Vázquez C (2006) Multiregion competition: a level set extension of region competition to multiple region image partitioning. Comput Vis Image Underst 101(3):137–150

    Article  Google Scholar 

  33. Mohand SA, Nizar B, Djemel Z (2008) Finite general Gaussian mixture modeling and application to image and video foreground segmentation. J Electr Imag 17(1):013005.

    Google Scholar 

  34. Mohand SA, Djemel Z, Nizar B, Sabri B (2010) Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection. IEEE Trans Circuit Syst Video Technol 20(10):1373–1377

    Google Scholar 

  35. Comaniciu D, Meer P (1997) Robust analysis of feature spaces: color image segmentation. In: IEEE conference computer vision and pattern recognition, pp 750–755

  36. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Google Scholar 

  37. Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans PAMI 26(11):1452–1458

    Article  Google Scholar 

  38. Deng Y, Manjunath B (2001) Unsupervised segmentation of color-texture regions in images and video. IEEE Trans PAMI 23 (8):800–810

    Article  Google Scholar 

  39. Ugarriza LG, Saber E, Vantaram SR, Amuso V, Shaw M, Bhaskar R (2009) Automatic image segmentation by dynamic region growth and multiresolution merging. IEEE Trans Image Process 18(10):2275–2288

    Article  MathSciNet  Google Scholar 

  40. Martinez-Uso A, Pla F, Garcia-Sevilla P (2006) Unsupervised image segmentation using a hierarchical clustering selection process. LNCS (4109):799–807

  41. Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans PAMI 29(6):929–944

    Article  Google Scholar 

  42. Zhang H, Fritts JE, Goldman SA (2004) An entropy-based objective evaluation method for image segmentation. In: Proceedings of the SPIE, pp 38–49

  43. Chen H-C, Wang S-J (2004) The use of visible color difference in the quantitative evaluation of color image segmentation. IEEE ICASSP 3:593–596

    Google Scholar 

  44. Zeboudj R (1998) Filtrage, seuillage automatique, contraste et contours: du pré-traitement à l’analyse d’image. Ph. D. thesis, University of Saint Etienne, France

  45. Rosenberger C, Chehdi K (2000) Genetic fusion: application to multi-components image segmentation. In: IEEE ICASSP, pp 2223–2226.

  46. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of 8th ICCV, vol 2, pp 416–423

  47. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Am Stat Assoc 32(200):675–701

    Article  Google Scholar 

  48. Jiang X, Marti C, Irniger C, Bunke H (2006) Distance measures for image segmentation evaluation. EURASIP J Appl Signal Process 2006:1–10

Download references

Acknowledgements

This work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007-00018, AYA2008-05965-C04-04/ESP and by Caixa-Castelló foundation under the project P1 1B2007-48. We would like to deeply thank Dr. Jason Fritts and Dr. Hui Zhang for their help towards implementing the unsupervised measures for evaluating the segmentation quality. We would also thank to Dr. Richard Nock, Dr. Sreenath Rao Vantaram and Dr. Pablo Arbelaez for their help detailing the SRM and GSEG algorithms and the Berkeley segmentation database respectively.

Author information

Authors and Affiliations

  1. Department of Computer Languages and Systems, Institute of New Imaging Technologies, Universitat Jaume I, Av. Sos Baynat s/n, 12071, Castellón, Spain

    Adolfo Martínez-Usó, Filiberto Pla & Pedro García-Sevilla

Authors
  1. Adolfo Martínez-Usó
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Filiberto Pla
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Pedro García-Sevilla
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Adolfo Martínez-Usó.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Martínez-Usó, A., Pla, F. & García-Sevilla, P. Unsupervised colour image segmentation by low-level perceptual grouping. Pattern Anal Applic 16, 581–594 (2013). https://doi.org/10.1007/s10044-011-0259-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-011-0259-1

Keywords