A Convolutional Neural Networks Based Coral Reef Annotation and Localization

EasyChair Preprint 5696, version 2

Abstract

The purpose of this study was to examine the effectiveness and flexibility of our image retrieval system by participating in the ImageCLEFcoral 2021 challenge. The system was developed for object detection and identification in any dataset. Initially, multiple trials were conducted to train the system with patterns of thirteen substrates and searching the relationships between them. Since the key for better performance of machine learning systems is repeated inputs, we used datasets with three distinct groups, each group having a different characterization of substrates. For submissions to the ImageCLEFcoral challenge, we tested the system with the provided test dataset, where it was able to find the patterns and relationships between the substrates in a massive amount of data that was also too complex. We sought to extract high-level image features and use deep learning and the CNN-RNN neural network. We obtained an acceptable range of accuracies for each characterization of substrates, although the average accuracy was 70 percent.

Keyphrases: Annotation and localization, Convolutional Neural Networks, coral reef, image classification

@booklet{EasyChair:5696,
  author    = {Rohit Raj Gunti and Abebe Rorissa},
  title     = {A Convolutional Neural Networks Based Coral Reef Annotation and Localization},
  howpublished = {EasyChair Preprint 5696},
  year      = {EasyChair, 2021}}