Journal article
ResFeats: Residual network based features for underwater image classification
Image and Vision Computing, Vol.93, Art. 103811
2020
Abstract
Oceanographers rely on advanced digital imaging systems to assess the health of marine ecosystems. The majority of the imagery collected by these systems do not get annotated due to lack of resources. Consequently, the expert labeled data is not enough to train dedicated deep networks. Meanwhile, in the deep learning community, much focus is on how to use pre-trained deep networks to classify out-of-domain images and transfer learning. In this paper, we leverage these advances to evaluate how well features extracted from deep neural networks transfer to underwater image classification. We propose new image features (called ResFeats) extracted from the different convolutional layers of a deep residual network pre-trained on ImageNet. We further combine the ResFeats extracted from different layers to obtain compact and powerful deep features. Moreover, we show that ResFeats consistently perform better than their CNN counterparts. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on MLC, Benthoz15, EILAT and RSMAS datasets.
Details
- Title
- ResFeats: Residual network based features for underwater image classification
- Authors/Creators
- A. Mahmood (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaS. An (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch UniversityF. Boussaid (Author/Creator) - The University of Western Australia
- Publication Details
- Image and Vision Computing, Vol.93, Art. 103811
- Publisher
- Elsevier BV
- Identifiers
- 991005542149507891
- Copyright
- © 2019 Elsevier B.V.
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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