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

Abstract

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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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). https://doi.org/10.1007/s40815-017-0305-2

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Keywords

  • 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