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An Automated Fish Species Identification System Based on Crow Search Algorithm

  • Gehad Ismail SayedEmail author
  • Aboul Ella Hassanien
  • Ahmed Gamal
  • Hassan Aboul Ella
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 723)

Abstract

This paper proposed an automated fish species identification system based on a modified crow search optimization algorithm. Median filtering is applied for image smoothing and removing noise through reducing the variation of intensities between the neighbors. Then, a k-mean clustering algorithm is used to segment the fish image into multiple segments. Shape-based and texture-based feature extraction process for classification is presented. A new modified binary version of crow search algorithm is proposed to reduce the data dimensionality of the extracted features. Finally, support vector machine and decision trees are implemented for classification and the fish species are classified based on either their class including Actinopterygii and Chondrichthyes or based on their order. Total of 270 images with different species, classes and orders are used for evaluation of the proposed system. The experimental results show that the proposed system achieves the highest classification accuracy compared to state-of-the-art algorithms. Also, the results show that the overall fish species identification system obtains on average of 10 folds, 96% classification accuracy for classification based on class and 74% for classification based on fish order.

Keywords

Crow Search Algorithm (CSA) Image classification Fish identification Feature selection 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computers and InformationCairo UniversityCairoEgypt
  2. 2.Faculty of Veterinary MedicineCairo UniversityCairoEgypt
  3. 3.Scientific Research Group in Egypt (SRGE)CairoEgypt

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