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 , 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.
|Title of host publication||Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|State||Published - 1 Jul 2017|
|Event||16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy|
Duration: 22 Oct 2017 → 29 Oct 2017
|Name||Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017|
|Conference||16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017|
|Period||22/10/17 → 29/10/17|
Bibliographical noteFunding Information:
This research has been partially funded by the European Union (CHIST-ERA IGLU), Spanish Government (projects DPI2015-65962-R, DPI2015-69376-R) and Aragon regional government (Grupo DGA T04-FSE). TT was supported by The Leona M. and Harry B. Helmsley Charitable Trust. The authors would like to give special thanks to Adi Zweifler for her help as an expert for hand-labeling some coral scene images.
© 2017 IEEE.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition