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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Fish Classification in Context of Noisy Images

verfasst von : Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne

Erschienen in: Engineering Applications of Neural Networks

Verlag: 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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Vorheriges Kapitel Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition
Nächstes Kapitel Neuro-Fuzzy Network for Modeling the Shoreline Realignment of the Kamari Beach, Santorini, Greece
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Metadaten
Titel
Fish Classification in Context of Noisy Images
verfasst von
Adamu Ali-Gombe
Eyad Elyan
Chrisina Jayne
Copyright-Jahr
2017
Buch
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-65171-2

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

Copyright-Jahr: 2017

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