Coral Reef Classification Using Improved WLD Feature Extraction with Convolution Neural Network Classification

In this paper, it is proposed to employ IWLD (Improved Weber Local Descriptor) for coral reef annotation. IWLD is a powerful texture descriptor, it is proposed to analyze the role of IWLD for coral reef classification. Background: Coral reefs are one of the oldest and dynamic ecosystems of the world. Manual annotation of coral reef is not possible due to lacking consistency and objectivity in human labeling. Objective: Manual annotation consumes an enormous number of hours to annotate every coral image and video frames as well as human resources. An emblematic survey states that more than 400 person-hours are required to annotate 1000 images. Incidentally, some coral species have different shapes, sizes and colors, while most of the corals seem indistinguishable to the human eye. In order to avoid the contradictory classifications, an expert system that can automatically annotate the corals is essential to improve the accuracy of classification. Method: The proposed improved WLD extract texture features from six combinations of color channels like (1) R Channel, (2) G Channel (3) B Channel (4) RG Channel (5) GB Channel and (6) BR Channel of an image in a holistic way while preserving their relations. The extracted features are analyzed and classified using CNN Classifier. Results: Experiments are carried out with EILAT, RSMAS, EILAT 2 and MLC2008 datasets and the proposed improved WLD based coral reef classification is found to be appropriate. From the accuracy point of view, the improved WLD demonstrate higher accuracy compared to other state-ofthe- art techniques. Conclusion: This paper analyzes the role of Improved WLD for feature extraction to classify coral reefs. For this purpose, EILAT, RSMAS, EILAT 2 and MLC2008 datasets have been used. It is observed that the proposed IWLD based classifier gives promising results for coral reef classification.