Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data
|dc.identifier.citation||Siddiqui, S. and Salman, A. and Malik, M. and Shafait, F. and Mian, A. and Shortis, M. and Harvey, E. 2018. Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES Journal of Marine Science. 75 (1): pp. 374-389.|
© International Council for the Exploration of the Sea 2017. All rights reserved. There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven classification models like neural networks require a huge amount of labelled data, otherwise they tend to over-fit to the training data and fail on unseen test data which is not involved in training. We present a state-of-the-art computer vision method for fine-grained fish species classification based on deep learning techniques. A cross-layer pooling algorithm using a pre-trained Convolutional Neural Network as a generalized feature detector is proposed, thus avoiding the need for a large amount of training data. Classification on test data is performed by a SVM on the features computed through the proposed method, resulting in classification accuracy of 94.3% for fish species from typical underwater video imagery captured off the coast of Western Australia. This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.
|dc.publisher||Oxford University Press 2009|
|dc.title||Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data|
|dcterms.source.title||ICES Journal of Marine Science|
|curtin.department||School of Molecular and Life Sciences (MLS)|
|curtin.accessStatus||Fulltext not available|
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