Abstract
In this paper, we analysed the performance of deep convolutional neural networks on noisy images of fish species. Thorough experiments using four variants of noisy and challenging dataset was carried out. Different deep convolutional models were evaluated. Firstly, we trained models on noisy dataset of fishing boat images. Our second approach trained the models on a new dataset generated by annotating fish instances only from the initial set of images. Lastly, we trained the models by synthesizing more data through the application of affine transforms and random noise. Results indicate that deep convolutional network performance deteriorate in the absence of well annotated training set. This opens direction for future research in automatic image annotation.
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Ali-Gombe, A., Elyan, E., Jayne, C. (2017). Fish Classification in Context of Noisy Images. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_19
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DOI: https://doi.org/10.1007/978-3-319-65172-9_19
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