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Gradient Based Aura Feature Extraction for Coral Reef Classification

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Abstract

Coral reefs, of all marine ecosystems, are the most diverse. They teem with life with almost a quarter of all ocean species depending upon reefs for food and shelter. Because of the immense diversity of coral reefs, they are often referred to as the ‘Rain-Forests of the Sea’. But due to the lack of consistency and objectivity in human labeling, the manual annotations of coral reefs are not conceivable. Multiple species of coral have coinciding characteristics and this makes automatic annotation, a challenging task. This research work is based on developing machine learning algorithms to employ Aura Matrix based on gradient based Cumulative Relative Difference which exploits the neighborhood relationship between pixels by comparing the relative pixel strength with respect to the center pixel. The objective of this paper is to show improvement of coral reef classification accuracy and that the proposed work outperforms the state-of-the art CNN classifiers on four coral datasets namely EILAT, EILAT 2, MLC 2010 and RSMAS.

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Acknowledgements

The authors are grateful to Prof. Peter. J. Edmunds who has provided us permission to use the MLC2010 dataset. The authors express sincere gratitude to Prof. ASM Shihavuddin for furnishing the datasets EILAT, EILAT2 RED SEA and RSMAS.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamilnadu, 627012, India

    M. Asha Paul, P. Arockia Jansi Rani & J. Liba Manopriya

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  1. M. Asha Paul
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  2. P. Arockia Jansi Rani
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  3. J. Liba Manopriya
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Correspondence to M. Asha Paul.

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Asha Paul, M., Arockia Jansi Rani, P. & Liba Manopriya, J. Gradient Based Aura Feature Extraction for Coral Reef Classification. Wireless Pers Commun 114, 149–166 (2020). https://doi.org/10.1007/s11277-020-07355-6

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