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.
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Sayed, G.I., Hassanien, A.E., Gamal, A., Ella, H.A. (2018). An Automated Fish Species Identification System Based on Crow Search Algorithm. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_12
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DOI: https://doi.org/10.1007/978-3-319-74690-6_12
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