Imperialist Competitive Algorithm-Based Optimization of Neuro-Fuzzy System Parameters for Automatic Red-eye Removal


There are great deals of consumer photographs which are affected by red-eye artifacts and arise frequently when shooting with flash. In this paper, a new technique is proposed to solve this problem. The proposed technique starts by detecting the skin-like regions using an optimized pixel-based neuro-fuzzy processing; morphological operations are then used to discard the extra areas after crossing the threshold. Once the skin regions are detected, five new features including geometric and color metrics are proposed to enhance the classification accuracy of the red-eye artifacts. After that, another optimized neuro-fuzzy classifier is employed to classify the red-eye regions by using the presented features. Final result is achieved by a definite syntax between skin and red-eye regions, and then, a simple correction method is used to correct the detected regions. Finally, a comparison is performed among the proposed method toward the other popular procedures and also a simple neuro-fuzzy. Final results showed the high performance of the proposed method.

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  1. 1.

    Huang, K.S., Chiu, P.J., Tsai, H.M., Kuo, C.C., Lee, H.Y., Wang, Y.C.: RedEye: preventing collisions caused by red-light running scooters with smartphones. IEEE Trans. Intell. Transp. Syst. 17(5), 1243–1257 (2016)

    Article  Google Scholar 

  2. 2.

    Wang, R., Zhou, Y., Chen, T., Zhou, M., Wang, L., Shaw, C.: Identification and functional analysis of a novel tryptophyllin peptide from the skin of the red-eye leaf frog. Agalychnis callidryas. Int. J. Bio. Sci. 11(2), 209 (2015)

    Article  Google Scholar 

  3. 3.

    Battiato, S., Farinella, G.M., Guarnera, M., Messina, G., Rav, D.: Red-eyes removal through cluster-based boosting on gray codes. EURASIP J. Image Video Process. (2010). doi:10.1155/2010/909043

    Google Scholar 

  4. 4.

    Revolutionizing Software. Remove red eye: Make your digital photos perfect! (2012)

  5. 5.

    Razmjooy, N., Naghibzadeh, S.S., Mousavi, B.S.: Automatic redeye correction in digital photos. Int. J. Comut. Appl. 95(9), 375–381 (2014)

    Google Scholar 

  6. 6.

    Safonov, I.V.: Automatic red eye detection. Redbot automatic red eye correction. (2007)

  7. 7.

    Ulichney, R., Gaubatz, M., Thong, J.V.: Redbot—a tool for improving red-eye correction. In: Proceedings of the IS&T/SID Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, Nov, Scottsdale, Arizona (2003)

  8. 8.

    Smolka, B., Czubin, K., Hardeberg, J.Y., Plataniotis, K.N., Szczepanski, M., Wojciechowski, K.: Towards automatic redeye effect removal. Pattern Recognit. Lett. 24, 1767–1785 (2003)

    Article  Google Scholar 

  9. 9.

    Hardeberg, J.Y.: Digital red eye removal. J. Imaging Sci. Technol. 46, 375–381 (2002)

    Google Scholar 

  10. 10.

    Hardeberg, J.Y.: Red-eye removal using color image processing, US Patent 6728401 (2004)

  11. 11.

    Czubin, K., Smolka, B., Szczepanski, M., Hardeberg, J.Y., Plataniotis, K.: On the redeye effect removal algorithm. In: Proceedings of the 1st European Conference on Color in Graphics, Imaging and Vision, pp. 292–297, April 3–5, 2002, Poitiers, France (2002)

  12. 12.

    Schildkraut, J.S., Gray, R.T.: A fully automatic redeye detection and correction algorithm. In: Proceedings of the International Conference on Image Processing, pp. 801–803, Sept 22–25, NY, USA, (2002)

  13. 13.

    Luo, H., Yen, J., Tretter, D.: An efficient automatic redeye detection and correction algorithm. In: Proceedings of the 17th International Conference on Pattern Recognition, pp. 883–886, Aug 23–26, Istanbul, Turkey, (2004)

  14. 14.

    Ghadimi, N.: A new hybrid algorithm based on optimal fuzzy controller in multimachine power system. Complexity 21(1), 78–93 (2015)

    MathSciNet  Article  Google Scholar 

  15. 15.

    Ghadimi, N.: An adaptive neuro-fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity. 21(1), 10–20 (2015)

    MathSciNet  Article  Google Scholar 

  16. 16.

    Hashemi, Farid, Ghadimi, Noradin, Sobhani, Behrooz: Islanding detection for inverter-based DG coupled with using an adaptive neuro-fuzzy inference system. Int. J. Electr. Power Energy Syst. 45(1), 443–455 (2013)

    Article  Google Scholar 

  17. 17.

    Hosseini, H., Tousi, B., Razmjooy, N., Khalilpour, M.: Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm. IETE J. Res. 1(596), 745–752 (2013)

    Article  Google Scholar 

  18. 18.

    Razmjooy, N., Mousavi, B.S., Soleymani, F.: A hybrid neural network imperialist competitive algorithm for skin color segmentation. Math. Comput. Model. 57(3), 848–856 (2013)

    Article  Google Scholar 

  19. 19.

    Foruzan, F., Payvandy, P.: A novel hybrid genetic and imperialist competitive algorithm for structure extraction of woven fabric images. J. Text. Instit. 1–13 (2016)

  20. 20.

    Kiani, A., Ebadi, H.: Development of a new method for edge detection from high-resolution aerial/satellite images, with emphasis on threshold optimization and using imperialist competitive algorithm. J. Geomat. Sci. Techol. 4(4), 67–82 (2015)

    Google Scholar 

  21. 21.

    Ahmadi, M.A., Ebadi, M., Shokrollahi, A., Majidi, S.M.: Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl. Soft Comput 13(2), 1085–1098 (2013)

    Article  Google Scholar 

  22. 22.

    Haibin, D., Huang, L.: Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning. Neurocomputing 125, 166–171 (2014)

    Article  Google Scholar 

  23. 23.

    Firouz, Mansour Hosseini, Ghadimi, Noradin: Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. J. Intell. Fuzzy Syst. 30(2), 845–859 (2016)

    Article  Google Scholar 

  24. 24.

    Hagh, Mehrdad Tarafdar, Ghadimi, Noradin: Multisignal histogram-based islanding detection using neuro-fuzzy algorithm. Complexity 21(1), 195–205 (2015)

    MathSciNet  Article  Google Scholar 

  25. 25.

    Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., Kişi, Ö.: Multiple linear regression, multi-layer perceptron network and adaptive neuro fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrol. Sci. J. 61(6), 1001–1009 (2016)

    Article  Google Scholar 

  26. 26.

    Chao, C., Robert, J., Jamie, T., Garibaldi, J.M.: An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), pp. 24–29 (2016)

  27. 27.

    Jasour, A.M., Atashpaz, E., Lucas, C.: Vehicle fuzzy controller design using imperialist competitive algorithm. In: Proceedinsgs of the 1st Iranian Joint Congress on Fuzzy and Intelligent Systems, Aug 29–31, Tehran, Iran (2008)

  28. 28.

    Razmjooy, N., Mousavi, B.S., Khalilpour, M., Hosseini, H.: Automatic selection and fusion of color spaces for image thresholding. Signal Image Video Process. (2012). doi:10.1007/s11760-012-0303-7

    Google Scholar 

  29. 29.

    Gill, J.S., Singh R., Palta P., Sharma T., Goel G.: CBIR of trademark images in different color spaces using XYZ and HSI. J. Netw. Commun. Emerg Technol. (JNCET). 6(5) (2016)

  30. 30.

    Mousavi, B.S., Sargolzaei, P., Razmjooy, N., Hosseinabadi, V., Soleymani, F.: Digital image segmentation using rule-base classifier. Am. J. Sci. Res. 35, 17–23 (2011)

    Google Scholar 

  31. 31.

    Razmjooy, N., Mousavi, B.S., Soleymani, F.: A real-time mathematical computer method for potato inspection using machine vision. Comput. Math. Appl. 63(1), 268–279 (2012)

    Article  MATH  Google Scholar 

  32. 32.

    Moallem, P., Razmjooy, N., Mousavi, B.S.: Robust potato color image segmentation using adaptive fuzzy inference system. Iran. J. Fuzzy Syst. 11(6), 47–65 (2014)

    MathSciNet  Google Scholar 

  33. 33.

    Lan, J., Zeng, Y.: Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram. Optik-Int. J. Light Electron Opt. 124(18), 3756–3760 (2013)

    Article  Google Scholar 

  34. 34.

    Raziq, A., Xu, A., Li, Y.: Automatic extraction of urban road centerlines from high-resolution satellite imagery using automatic thresholding and morphological operation method. J. Geogr. Inf. Syst 8(04), 517 (2016)

    Google Scholar 

  35. 35.

    Michael Z.: The Red iGone tool. (2010)

  36. 36. company tool, (2016):

  37. 37.

    Donghui, W.: Automatic red eye removal, US patent application, pp. 1–6, 2005/0232481. CA, US (2005)

  38. 38.

    CyberLink.: CyberLink offers auto red-eye removal technology in its digital home products. (2004)

  39. 39.

    Arc Soft.: How to remove red eyes in photo on Window/Mac.

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Correspondence to Noradin Ghadimi.

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Razmjooy, N., Ramezani, M. & Ghadimi, N. Imperialist Competitive Algorithm-Based Optimization of Neuro-Fuzzy System Parameters for Automatic Red-eye Removal. Int. J. Fuzzy Syst. 19, 1144–1156 (2017).

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  • Red-eye correction
  • HSI color space
  • Skin detection
  • Neuro-fuzzy classification
  • Subtractive clustering
  • Imperialist competitive algorithm
  • Morphological operations
  • Color and geometry features
  • Kapur’s threshold