Weakly supervised underwater fish segmentation using affinity LCFCN
Laradji, Issam H., Saleh, Alzayat, Rodriguez, Pau, Nowrouzezahrai, Derek, Rahimi Azghadi, Mostafa, and Vazquez, David (2021) Weakly supervised underwater fish segmentation using affinity LCFCN. Scientific Reports, 11. p. 17379.
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting productivity in marine and aquaculture applications. Some methods are based on manual collection of these measurements using tools like a ruler which is time consuming and labour intensive. Others rely on fully-supervised segmentation models to automatically acquire these measurements but require collecting per-pixel labels which are also time consuming. It can take up to 2 minutes per fish to acquire accurate segmentation labels. To address this problem, we propose a segmentation model that can efficiently train on images labeled with point-level supervision, where each fish is annotated with a single click. This labeling scheme takes an average of only 1 second per fish. Our model uses a fully convolutional neural network with one branch that outputs per-pixel scores and another that outputs an affinity matrix. These two outputs are aggregated using a random walk to get the final, refined per-pixel output. The whole model is trained end-to-end using the localization-based counting fully convolutional neural network (LCFCN) loss and thus we call our method Affinity-LCFCN (A-LCFCN). We conduct experiments on the DeepFish dataset, which contains several fish habitats from north-eastern Australia. The results show that A-LCFCN outperforms a fully-supervised segmentation model when the annotation budget is fixed. They also show that A-LCFCN achieves better segmentation results than LCFCN and a standard baseline.
| Item ID: | 69294 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 2045-2322 |
| Related URLs: | |
| Copyright Information: | Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2021 |
| Funders: | Australian Research Training Program (RTP) Scholarship |
| Date Deposited: | 08 Sep 2021 23:05 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 100% |
| SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 50% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50% |
| Downloads: |
Total: 775 Last 12 Months: 8 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |