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- Title
Semantic Image Segmentation Using Scant Pixel Annotations.
- Authors
Chakravarthy, Adithi D.; Abeyrathna, Dilanga; Subramaniam, Mahadevan; Chundi, Parvathi; Gadhamshetty, Venkataramana
- Abstract
The success of deep networks for the semantic segmentation of images is limited by the availability of annotated training data. The manual annotation of images for segmentation is a tedious and time-consuming task that often requires sophisticated users with significant domain expertise to create high-quality annotations over hundreds of images. In this paper, we propose the segmentation with scant pixel annotations (SSPA) approach to generate high-performing segmentation models using a scant set of expert annotated images. The models are generated by training them on images with automatically generated pseudo-labels along with a scant set of expert annotated images selected using an entropy-based algorithm. For each chosen image, experts are directed to assign labels to a particular group of pixels, while a set of replacement rules that leverage the patterns learned by the model is used to automatically assign labels to the remaining pixels. The SSPA approach integrates active learning and semi-supervised learning with pseudo-labels, where expert annotations are not essential but generated on demand. Extensive experiments on bio-medical and biofilm datasets show that the SSPA approach achieves state-of-the-art performance with less than 5% cumulative annotation of the pixels of the training data by the experts.
- Subjects
IMAGE segmentation; SEMANTIC computing; SUPERVISED learning; IMAGE processing; COMPUTER vision
- Publication
Machine Learning & Knowledge Extraction, 2022, Vol 4, Issue 3, p621
- ISSN
2504-4990
- Publication type
Article
- DOI
10.3390/make4030029