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Current coral reef health monitoring programs rely on biodiversity data obtained through the acquisition and annotation of underwater photographs. Manual annotation of these photographs is a necessary step, but has become problematic due to the high volume of images and the lack of available human resources. While automated and reliable multi-spectral annotation methods exist, coral reef images are often limited to visible light, which makes automation difficult. Much of the previous work has focused on popular texture recognition methods, but the results remain unsatisfactory when compared to human performance for the same task. In this work, we present an improved automatic method for coral image annotation that yields consistent accuracy improvements over existing methods. Our method builds on previous work by combining multiple feature representations. We demonstrate that the aggregation of multiple methods outperforms any single method. Furthermore, our proposed system requires virtually no parameter tuning, and supports rejection for improved results. Firstly, the complex texture diversity of corals is handled by combining multiple feature representations: local binary patterns, hue and opponent angle histograms, textons, and deep convolutional activation feature. Secondly, these multiple representations are aggregated using a score-level fusion of multiple support vector machines. Thirdly, rejection can optionally be applied to enhance classification results, and allows efficient semi-supervised image annotation in collaboration with human experts.
This paper is a follow up on "Blanchet, JN. Automated texture-based recognition of corals in natural scene images (Master's thesis), École de Technologie Supérieure, Montreal (Canada), 2016", and presents evidence to support that the combination of multiple texture representations methods perform better than any single method for the task of automated coral reef image annotation.
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