Gradient Based Aura Feature Extraction for Coral Reef Classification

• Published in: Wireless personal communications Vol. 114; no. 1; pp. 149 - 166
• Main Authors: Asha Paul, M., Arockia Jansi Rani, P., Liba Manopriya, J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Algorithms; Annotations; Classification; Communications Engineering; Computer Communication Networks; Coral reefs; Engineering; Feature extraction; Machine learning; Networks; Pixels; Signal,Image and Speech Processing; Dice similarity; Coral reef; Aura matrix; Active contour; Classification; g-CRD
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-020-07355-6

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.