Coral Reef Image Classification Employing Deep Features and A Novel Local Inter Cross Weber Magnitude (LICWM) Pattern

Coral reefs are essential in marine ecosystem as they sustain a great part of marine life. The automatic classification of corals on submarine images is so much important in recent times. Hence, it can assist marine experts to classify endangered and susceptible coral reefs. But, classifying coral reef images is a promising task due to its varying color, texture, shape and morphology. The main objective of this work is to propose a novel operator called Local Inter Cross Weber Magnitude (LICWM) pattern. For classification, VGG–16 architecture is applied for extracting the features of coral reef images. VGG- 16 architecture has many layers for extracting deep features effectively. The traditional methods used K-Nearest Neighbor (KNN) and Random Forest (RF) for classifying deep features. The performance of the proposed method is estimated using F-score. The Experimental results show that the proposed operator achieves better accuracy level and performance with EILAT and RSMAS data sets.