Advertisement

Neural Computing and Applications

, Volume 30, Issue 3, pp 871–889 | Cite as

Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity

  • Farid García-Lamont
  • Jair Cervantes
  • Asdrúbal López-Chau
Original Article

Abstract

Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.

Keywords

Self-organizing maps Color classification Image segmentation Color spaces 

Notes

Acknowledgements

This work was sponsored by Secretaría de Educación Pública: convenio PROMEP/103.5/13/6535. We thank Francisco Gallegos Funes for his valuable help and support.

References

  1. 1.
    Gökmen V, Sügüt I (2007) A non-contact computer vision based analysis of color in foods. Int J Food Eng 3(5). doi: 10.2202/1556-3758.1129
  2. 2.
    Lopez JJ, Cobos M, Aguilera E (2011) Computer-based detection and classification of flaws in citrus fruits. Neural Comput Appl 20(7):975–981CrossRefGoogle Scholar
  3. 3.
    Lepistö L, Kuntuu I, Visa A (2005) Rock image classification using color features in Gabor space. J Electron Imaging 14(4):1–3CrossRefGoogle Scholar
  4. 4.
    Ghoneim DM (2011) Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions. Theor Biol Med Model 8:25CrossRefGoogle Scholar
  5. 5.
    Harrabi R, Braiek EB (2012) Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images. EURASIP J Image Video Process 2012:11. doi: 10.1186/1687-5281-2012-11 CrossRefGoogle Scholar
  6. 6.
    Lingala M, Stanley RJ, Rader RK, Hagerty J, Rabinovitz HS, Oliveiro M, Choudhry I, Stoecker WV (2014) Fuzzy logic color detection: blue areas in melanoma dermoscopy images. Comput Med Imaging Graph 38(5):403–410CrossRefGoogle Scholar
  7. 7.
    Wang F, Man L, Wang B, Xiao Y, Pan W, Lu X (2008) Fuzzy-based algorithm for color recognition of license plates. Pattern Recognit Lett 29(7):1007–1020CrossRefGoogle Scholar
  8. 8.
    del Fresno M, Macchi A, Marti Z, Dick A, Clausse A (2006) Application of color image segmentation to estrusc detection. J Vis 9(2):171–178CrossRefGoogle Scholar
  9. 9.
    Rotaru C, Graf T, Zhang J (2008) Color image segmentation in HSI space for automotive applications. J Real Time Image Process 3(4):311–322CrossRefGoogle Scholar
  10. 10.
    Bianconi F, Fernández A, González E, Saetta SA (2013) Performance analysis of colour descriptors for parquet sorting. Expert Syst Appl 40(5):1636–1644CrossRefGoogle Scholar
  11. 11.
    Aghbarii ZA, Haj RA (2006) Hill-manipulation: an effective algorithm for color image segmentation. Image Vis Comput 24(8):498–903CrossRefGoogle Scholar
  12. 12.
    Mignotte M (2014) A non-stationary MRF model for image segmentation from a soft boundary map. Pattern Anal Appl 17(1):129–139MathSciNetCrossRefGoogle Scholar
  13. 13.
    Liu Z, Song YQ, Chen JM, Xie CH, Zhu F (2012) Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials. Neural Comput Appl 21(4):801–811CrossRefGoogle Scholar
  14. 14.
    Mousavi BS, Soleymani F, Razmjooy N (2013) Color image segmentation using neuro-fuzzy system in a novel optimized color space. Neural Comput Appl 23(5):1513–1520CrossRefGoogle Scholar
  15. 15.
    Ong S, Yeo N, Lee K, Venkatesh Y, Cao D (2002) Segmentation of color images using a two-stage self-organizing network. Image Vis Comput 20(4):279–289CrossRefGoogle Scholar
  16. 16.
    Jiang Y, Zhou ZH (2004) SOM ensemble-based image segmentation. Neural Process Lett 20(3):171–178CrossRefGoogle Scholar
  17. 17.
    Khan A, Jaffar MA (2015) Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation. Appl Soft Comput 32:300–310CrossRefGoogle Scholar
  18. 18.
    Araujo A, Costa DC (2009) Local adaptive receptive field self-organizing map for image color segmentation. Image Vis Comput 27(9):1229–1239MathSciNetCrossRefGoogle Scholar
  19. 19.
    Stephanakis IM, Anastassopoulos GC, Iliadis LS (2010) Color segmentation using self-organizing feature maps (SOFMs) defined upon color and spatial image space. In: Artificial neural networks—ICANN 2010, lecture notes on computer science (LNCS), vol 6352, pp 500–510Google Scholar
  20. 20.
    Khan A, Ullah J, Jaffar MA, Choi TS (2014) Color image segmentation: a novel spatial fuzzy genetic algorithm. Signal Image Video Process 8(7):1233–1243CrossRefGoogle Scholar
  21. 21.
    Khan A, Jaffar MA, Choi TS (2013) SOM and fuzzy based color image segmentation. Multimed Tools Appl 64(2):331–344CrossRefGoogle Scholar
  22. 22.
    Wang L, Dong M (2012) Multi-level low-rank approximation-based spectral clustering for image segmentation. Pattern Recognit Lett 33(16):2206–2215CrossRefGoogle Scholar
  23. 23.
    Mújica-Vargas D, Gallegos-Funes FJ, Rosales-Silva AJ (2013) A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognit Lett 34(4):400–413CrossRefGoogle Scholar
  24. 24.
    Huang R, Sang N, Luo D, Tang Q (2011) Image segmentation via coherent clustering in L*a*b* color space. Pattern Recognit Lett 32(7):891–902CrossRefGoogle Scholar
  25. 25.
    Nadernejad E, Sharifzadeh S (2013) A new method for image segmentation based on fuzzy c-means algorithm on pixonal images formed by bilateral filtering. Signal Image Video Process 7(5):855–863CrossRefGoogle Scholar
  26. 26.
    Guo Y, Sengur A (2013) A novel color image segmentation approach based on neutrosophic set and modified fuzzy c-means. Circuits Syst Signal Process 32(4):1699–1723MathSciNetCrossRefGoogle Scholar
  27. 27.
    Kim JY (2014) Segmentation of lip region in color images by fuzzy clustering. Int J Control Autom Syst 12(3):652–661CrossRefGoogle Scholar
  28. 28.
    Ito S, Yoshioka M, Omatu S, Kita K, Kugo K (2006) An image segmentation method using histograms and the human characteristics of HSI color space for a scene image. Artif Life Robot 10(1):6–10CrossRefGoogle Scholar
  29. 29.
    Mignotte M (2010) Penalized maximum rand estimator for image segmentation. IEEE Trans Image Process 19(6):1610–1624MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Rashedi E, Nezamabadi-pour H (2013) A stochastic gravitational approach to feature based color. Eng Appl Artif Intell 26(4):1322–1332CrossRefGoogle Scholar
  31. 31.
    Mignotte M, Hélou C (2014) A precision–recall criterion based consensus model for fusing multiple segmentations. Int J Signal Process Image Process Pattern Recognit 7(3):61–82Google Scholar
  32. 32.
    Xue A, Jia C (2009) A new method of color map segmentation based on the self-organizing neural network. In: Emerging intelligent computing technology and applications. With aspects of artificial intelligence, lecture notes on artificial intelligence (LNAI), vol 5755, pp 417–423Google Scholar
  33. 33.
    Halder A, Dalmiya S, Sadhu T (2014) Color image segmentation using semi-supervised self-organizing feature map. Adv Signal Process Intell Recognit Syst 264:591–598CrossRefGoogle Scholar
  34. 34.
    Sima H, Guo P, Liu L (2011) Scale estimate of self-organizing map for color image segmentation. IEEE Int Conf Syst Man Cybern 1491–1495. doi: 10.1109/ICSMC.2011.6083882
  35. 35.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  36. 36.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480Google Scholar
  37. 37.
    Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 110(2):260–280CrossRefGoogle Scholar
  38. 38.
    Estrada FJ, Jepson AD (2009) Benchmarking image segmentation algorithms. Int J Comput Vis 85(2):167–181CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Farid García-Lamont
    • 1
  • Jair Cervantes
    • 1
  • Asdrúbal López-Chau
    • 2
  1. 1.Universidad Autónoma del Estado de México, Centro Universitario UAEM TexcocoTexcoco-Estado de MéxicoMexico
  2. 2.Universidad Autónoma del Estado de México, Centro Universitario UAEM ZumpangoZumpango-Estado de MéxicoMexico

Personalised recommendations