Statistical Modeling based Directional Pattern Design (SMDPD) feature extraction for coral reef classification

Coral reefs are a precious ecosystem that supports majority of marine life. The identification of coral species is essential in the conservation and monitoring process. Distinguishing the coral species among the coral reef family is really a challenging task since they have analogous characteristics and have complex spatial borders between the coral classes. This requires experts to identify corals. But due to inconsistency and biasing of manual labelling, the manual annotations of coral reefs are not feasible. The objective of this research work is to identify various types of corals present in the given input video. This work is aimed at identifying thirty-six types of coral by employing a new feature extraction method called Statistical Modeling based Directional Pattern Design (SMDPD) using a new directional pattern. The proposed work outperforms the state-of-art techniques for four coral datasets namely EILAT, EILAT 2 Red Sea, MLC 2010 and RSMAS. Another advantage of this work is the reduction in feature bin size from 255 to just 16 bins.