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2017 | OriginalPaper | Chapter

Fish Classification in Context of Noisy Images

Authors : Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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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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Title: Fish Classification in Context of Noisy Images
Authors: Adamu Ali-Gombe
Eyad Elyan
Chrisina Jayne
Publisher: Springer International Publishing
Book: Engineering Applications of Neural Networks
Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-65172-9_19

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