MDNet: Multi-Patch Dense Network for Coral Classification

Md Modasshir, Alberto Quattrini Li, Ioannis Rekleitis
In MTS/IEEE OCEANS - Charleston 2018.


Abstract: Classifying coral species from visual data is a challenging task due to significant intra-species variation, high interspecies similarity, inconsistent underwater image clarity, and high dataset imbalance. In addition, point annotation, the labeling method used for coral reef images by marine biologists, is prone to mislabeling. Point annotation also makes existing datasets incompatible with state-of-the-art classification methods which use the bounding box annotation technique. In this paper, we present a novel end-to-end Convolutional Neural Network (CNN) architecture, Multi-Patch Dense Network (MDNet) that can learn to classify coral species from point annotated visual data. The proposed approach utilizes patches of different scale centered on point annotated objects. Furthermore, MDNet utilizes dense connectivity among layers to reduce over-fitting on imbalanced datasets. Experimental results on the Moorea Labeled Coral (MLC) benchmark dataset are presented. The proposed MDNet achieves higher accuracy and average class precision than the state-of-the-art approaches.



  title        = {MDNet: Multi-Patch Dense Network for Coral
  author       = {Md Modasshir and Alberto Quattrini Li and Ioannis
  booktitle    = {MTS/IEEE OCEANS - Charleston},
  pages        = {1--6},
  year	       = {2018},
  organization = {IEEE}

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