Coral-Segmentation: Training Dense Labeling Models with Sparse Ground Truth

Iñigo Alonso, Ana Cambra, Adolfo Muñoz, Tali Treibitz, Ana C. Murillo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Biological datasets, such as our case of study, coral segmentation, often present scarce and sparse annotated image labels. Transfer learning techniques allow us to adapt existing deep learning models to new domains, even with small amounts of training data. Therefore, one of the main challenges to train dense segmentation models is to obtain the required dense labeled training data. This work presents a novel pipeline to address this pitfall and demonstrates the advantages of applying it to coral imagery segmentation. We fine tune state-of-the-art encoder-decoder CNN models for semantic segmentation thanks to a new proposed augmented labeling strategy. Our experiments run on a recent coral dataset [4], proving that this augmented ground truth allows us to effectively learn coral segmentation, as well as provide a relevant score of the segmentation quality based on it. Our approach provides a segmentation of comparable or better quality than the baseline presented with the dataset and a more flexible end-to-end pipeline.

Original languageAmerican English
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2874-2882
Number of pages9
ISBN (Electronic)9781538610343
DOIs
StatePublished - 1 Jul 2017
Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volume2018-January

Conference

Conference16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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