Skip to main content

Advertisement

SpringerLink
Log in

Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Clustering validity index (CVI) plays an important role in data partitioning and image segmentation. In this paper, a new CVI is proposed to perform the color image segmentation. The proposed CVI combines compactness, separation and overlap to assess the clustering quality effectively. The aggregation operators (t-norms and t-conorms) are used to build a new reliable and robust overlap measure. Moreover, a genetic algorithm is employed to dynamically optimize the clusters centroids and get the best possible data partition. The clustering of super-pixels is performed to reduce the computational cost and convergence time. The genetic algorithm with new clustering validity index is able to find the best data partitioning. The performance of the proposed algorithm is evaluated on the Berkeley image segmentation database. The extensive experimentation shows that the proposed algorithm performs better compared to other state-of-the-art methods.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Zhang, X., Su, H., Yang, L., Zhang, S.: Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5361–5368 (2015)

  2. Belloulata, K., Belallouche, L., Belalia, A., Kpalma, K.: Region based image retrieval using shape-adaptive dct. In: 2014 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 470–474. IEEE (2014)

  3. Ahmad, J., Sajjad, M., Mehmood, I., Rho, S., Baik, S.W.: Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J. Real Time Image Process. 13(3), 431–447 (2017)

    Article  Google Scholar 

  4. Tsochatzidis, L., Zagoris, K., Arikidis, N., Karahaliou, A., Costaridou, L., Pratikakis, I.: Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognit. 71, 106–117 (2017)

    Article  Google Scholar 

  5. Ghosh, N., Agrawal, S., Motwani, M.: A survey of feature extraction for content-based image retrieval system. In: Proceedings of International Conference on Recent Advancement on Computer and Communication, pp. 305–313. Springer (2018)

  6. Irtaza, A., Adnan, S.M., Ahmed, K.T., Jaffar, A., Khan, A., Javed, A., Mahmood, M.T.: An ensemble based evolutionary approach to the class imbalance problem with applications in CBIR. Appl. Sci. 8(4), 495 (2018)

    Article  Google Scholar 

  7. Hsu, W.-Y.: Segmentation-based compression: new frontiers of telemedicine in telecommunication. Telemat. Inform. 32(3), 475–485 (2015)

    Article  Google Scholar 

  8. Akbari, M., Liang, J., Han, J.: Dsslic: deep semantic segmentation-based layered image compression. arXiv:1806.03348 (2018)

  9. Song, X., Huang, Q., Chang, S., He, J., Wang, H.: Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction. Med. Biol. Eng. Comput. 56(6), 957–966 (2018)

    Article  Google Scholar 

  10. Li, S., Wang, J., Zhu, Q.: Adaptive bit plane quadtree-based block truncation coding for image compression. In: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), vol 10615, p. 106151K. International Society for Optics and Photonics (2018)

  11. Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Schwarz, M., Milan, A., Periyasamy, A.S., Behnke, S.: RGB-D object detection and semantic segmentation for autonomous manipulation in clutter. Int. J. Robot. Res. 37(4–5), 437–451 (2018)

    Article  Google Scholar 

  13. Wei, Y., Shen, Z., Cheng, B., Shi, H., Xiong, J., Feng, J, Huang, T.: Ts2c: tight box mining with surrounding segmentation context for weakly supervised object detection. In: European Conference on Computer Vision, pp. 454–470. Springer, Cham (2018)

  14. Wang, W., Shen, J., Shao, L.: Video salient object detection via fully convolutional networks. IEEE Trans. Image Process. 27(1), 38–49 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang, W., Shen, J., Yang, R., Porikli, F.: Saliency-aware video object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1, 20–33 (2018)

    Article  Google Scholar 

  16. Wang, W., Shen, J., Porikli, F., Yang, R.: Semi-supervised video object segmentation with super-trajectories. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https://doi.org/10.1109/TPAMI.2018.2819173

  17. Ullah, J., Khan, A., Jaffar, M.A.: Motion cues and saliency based unconstrained video segmentation. Multimed. Tools Appl. 77(6), 7429–7446 (2018)

    Article  Google Scholar 

  18. Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Learning to track for spatio-temporal action localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3164–3172 (2015)

  19. Mosabbeb, E.A., Cabral, R., De la Torre, F., Fathy, M.: Multi-label discriminative weakly-supervised human activity recognition and localization. In: Asian Conference on Computer Vision, pp. 241–258. Springer (2014)

  20. Gu, F., Khoshelham, K., Valaee, S., Shang, J., Zhang, R.: Locomotion activity recognition using stacked denoising autoencoders. IEEE Internet Things J. 5(3), 2085–2093 (2018)

    Article  Google Scholar 

  21. Noor, M.H.M., Salcic, Z., Kevin, I., Wang, K.: Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Pervasive Mob. Comput. 38, 41–59 (2017)

    Article  Google Scholar 

  22. Triboan, D., Chen, L., Chen, F., Wang, Z.: Semantic segmentation of real-time sensor data stream for complex activity recognition. Personal Ubiquitous Comput. 21(3), 411–425 (2017)

    Article  Google Scholar 

  23. Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., Kim, D.: Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognit. 61, 295–308 (2017)

    Article  Google Scholar 

  24. Shen, J., Du, Y., Li, X., et al.: Interactive segmentation using constrained Laplacian optimization. IEEE Trans. Circuits Syst. Video Technol. 24(7), 1088–1100 (2014)

    Article  Google Scholar 

  25. Shen, J., Peng, J., Dong, X., Shao, L., Porikli, F.: Higher order energies for image segmentation. IEEE Trans. Image Process. 26(10), 4911–4922 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  26. Dong, X., Shen, J., Shao, L., Van Gool, L.: Sub-markov random walk for image segmentation. IEEE Trans. Image Process. 25(2), 516–527 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  27. Peng, J., Shen, J., Li, X.: High-order energies for stereo segmentation. IEEE Trans. Cybern. 46(7), 1616–1627 (2016)

    Article  Google Scholar 

  28. Ciesielski, K.C., Miranda, P.A.A., Falcão, A.X., Udupa, J.K.: Joint graph cut and relative fuzzy connectedness image segmentation algorithm. Med. Image Anal. 17(8), 1046–1057 (2013)

    Article  Google Scholar 

  29. Spina, T.V., de Miranda, P.A.V., Falcao, A.X.: Hybrid approaches for interactive image segmentation using the live markers paradigm. IEEE Trans. Image Process. 23(12), 5756–5769 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Kumar, S.N., Fred, A.L., Varghese, P.S.: Suspicious lesion segmentation on brain, mammograms and breast MR images using new optimized spatial feature based super-pixel fuzzy c-means clustering. J. Digit. Imaging (2018). https://doi.org/10.1007/s10278-018-0149-9

  31. Zhao, F., Liu, H., Fan, J., Chen, C.W., Lan, R., Li, N.: Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation. Neurocomputing 312(27), 296–309 (2018)

    Article  Google Scholar 

  32. Khan, A., Ullah, J., Jaffar, M.A., Choi, T.S.: Color image segmentation: a novel spatial fuzzy genetic algorithm. Signal Image Video Process. 8(7), 1233–1243 (2014)

    Article  Google Scholar 

  33. Khan, A., Jaffar, M.A., Shao, L.: A modified adaptive differential evolution algorithm for color image segmentation. Knowl. Inf. Syst. 43(3), 583–597 (2015)

    Article  Google Scholar 

  34. Khan, A., Jaffar, M.A., Choi, T.S.: Som and fuzzy based color image segmentation. Multimed. Tools Appl. 64(2), 331–344 (2013)

    Article  Google Scholar 

  35. Vazquez, E., Baldrich, R., Van De Weijer, J., Vanrell, M.: Describing reflectances for color segmentation robust to shadows, highlights, and textures. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 917–930 (2011)

    Article  Google Scholar 

  36. Lee, S., Kim, J., Lim, H., Ahn, S.C.: Surface reflectance estimation and segmentation from single depth image of ToF camera. Signal Process. Image Commun. 47, 452–462 (2016)

    Article  Google Scholar 

  37. Pérez-Carrasco, J.A., Acha-Piñero, B., Serrano-Gotarredona, C., Gevers, T.:. Reflectance-based segmentation using photometric and illumination invariants. In: International Conference Image Analysis and Recognition, pp. 179–186. Springer (2014)

  38. Bruls, T., Maddern, W., Morye, A.A., Newman, P.: Mark yourself: road marking segmentation via weakly-supervised annotations from multimodal data. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1863–1870. IEEE (2018)

  39. Garces, E., Reinhard, E.: Light-field surface color segmentation with an application to intrinsic decomposition. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1480–1488. IEEE (2018)

  40. Qin, A.K., Clausi, D.A.: Multivariate image segmentation using semantic region growing with adaptive edge penalty. IEEE Trans. Image Process. 19(8), 2157–2170 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  41. Plath, N., Toussaint, M., Nakajima, S.: Multi-class image segmentation using conditional random fields and global classification. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 817–824 (2009)

  42. Bejar, H.H.C., Miranda, P.A.V.: Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods. EURASIP J. Image Video Process. 2015(1), 21 (2015)

    Article  Google Scholar 

  43. Kumar, M.J., Raj Kumar, G.V.S.: Hybrid image segmentation model based on active contour and graph cut with fuzzy entropy maximization. Int. J. Appl. Eng. Res. 12(23), 13623–13637 (2017)

    Google Scholar 

  44. Wang, F., Yan, W., Li, M., Zhang, P., Zhang, Q.: Adaptive hybrid conditional random field model for sar image segmentation. IEEE Trans. Geosci. Remote Sens. 55(1), 537–550 (2017)

    Article  Google Scholar 

  45. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  46. Nascimento, M.C.V., De Carvalho, A.C.P.L.F.: Spectral methods for graph clustering a survey. Eur. J. Oper. Res. 211(2), 221–231 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  47. Rajkumar, G.V., Rao, K.S., Rao, P.S.: Image segmentation method based on finite doubly truncated bivariate gaussian mixture model with hierarchical clustering. Int. J. Comput. Sci. 8(4), 151–110 (2011)

    Google Scholar 

  48. Yanga, H.Y., Wanga, X.Y., Wanga, Q.Y., Zhanga, X.J.: LS-SVM based image segmentation using color and texture information. J. Vis. Commun. Image Represent. 23(7), 1095–1112 (2012)

    Article  Google Scholar 

  49. Patel, J., Doshi, K.: A study of segmentation methods for detection of tumor in brain MRI. Adv. Electron. Electr. Eng. 4(3), 279–284 (2014)

    Google Scholar 

  50. Guo, Y., Xia, R., Şengür, A., Polat, K.: A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering. Neural Comput. Appl. 28(10), 3009–3019 (2017)

    Article  Google Scholar 

  51. Vendramin, L., Campello, R.J., Hruschka, E.R.: Relative clustering validity criteria: a comparative overview. Stat. Anal. Data Min. 3(4), 209–235 (2010)

    MathSciNet  Google Scholar 

  52. Starczewski, A.: A new validity index for crisp clusters. Pattern Anal. Appl. 20(3), 687–700 (2017)

    Article  MathSciNet  Google Scholar 

  53. Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  54. Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.: Real-time superpixel segmentation by dbscan clustering algorithm. IEEE Trans. Image Process. 25(12), 5933–5942 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  55. Dong, X., Shen, J., Shao, L.: Hierarchical superpixel-to-pixel dense matching. IEEE Trans. Circuits Syst. Video Technol. 27(12), 2518–2526 (2017)

    Article  Google Scholar 

  56. Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2097–2104 (2011)

  57. Bezdek, J.C.: Cluster validity with fuzzy sets. J. Cybern. 3(3), 58–73 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  58. Mascarilla, L., Berthier, M., Frélicot, C.: A k-order fuzzy or operator for pattern classification with k-order ambiguity rejection. Fuzzy Sets Syst. 159(15), 2011–2029 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  59. Khan, A., Jaffar, M.A.: Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation. Appl. Soft Comput. 32, 300–310 (2015)

    Article  Google Scholar 

  60. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  61. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceeding of the IEEE International Conference on Computer Vision, vol 2, pp. 416–423 (2001)

  62. Yang, A.Y., Wright, J., Ma, Y., Sastry, S.S.: Unsupervised segmentation of natural images via lossy data compression. In: Computer Vision and Image Understanding, pp. 212–225 (2008)

  63. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(02), 167–181 (2004)

    Article  Google Scholar 

  64. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)

    Article  Google Scholar 

  65. Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  66. Salah, M.B., Mitiche, A., Ayed, I.B.: Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans. Image Process. 20(2), 545–557 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  67. Meila, M.: Comparing clusterings by the variation of information. In: Schölkopf, B., Warmuth, M.K. (eds.) Learning theory and kernel machines, Lecture Note in Computer Science, vol. 2777, pp. 173–187. Springer, Berlin, Heidelberg (2003)

    Chapter  Google Scholar 

  68. Unnikrishnan, R., Hebert, M.: Measures of similarity. In: Proceedings of the IEEE Workshop on Computer Vision Applications, vol 1, pp. 394–401 (2005)

  69. Wang, W., Shen, J.: Deep visual attention prediction. IEEE Trans. Image Process. 27(5), 2368–2378 (2018)

    Article  MathSciNet  Google Scholar 

  70. Wang, W., Shen, J., Ling, H.: A deep network solution for attention and aesthetics aware photo cropping. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https://doi.org/10.1109/TPAMI.2018.2840724

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, COMSATS University Islamabad (CUI), Abbottabad, Pakistan

    Ahmad Khan, Zia ur Rehman & Ahmad Din

  2. Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Muhammad Arfan Jaffar

  3. National University of Computer and Emerging Sciences, H-11/4, Islamabad, Pakistan

    Javid Ullah

  4. National Database and Registration Authority (NADRA) Regional Head Office (RHO), Peshawar, KP, Pakistan

    Akbar Ali

  5. Department of Computer Science, University of Buner, Buner, KP, Pakistan

    Niamat Ullah

Authors
  1. Ahmad Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zia ur Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Arfan Jaffar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javid Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ahmad Din
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Akbar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Niamat Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Khan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., ur Rehman, Z., Jaffar, M.A. et al. Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI). SIViP 13, 833–841 (2019). https://doi.org/10.1007/s11760-019-01419-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01419-2

Keywords

Search

Navigation