| Full text | |
| Author(s): |
Benato, Barbara C.
;
Telea, Alexandru C.
;
Falcao, Alexandre X.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2018-01-01. |
| Abstract | |
While the number of unsupervised samples for data annotation is usually high, the absence of large supervised training sets for effective feature learning and design of high-quality classifiers is a known problem whenever specialists are required for data supervision. By exploring the feature space of supervised and unsupervised samples, semi-supervised learning approaches can usually improve the classification system. However, these approaches do not usually exploit the pattern-finding power of the user's visual system during machine learning. In this paper, we incorporate the user in the semi-supervised learning process by letting the feature space projection of unsupervised and supervised samples guide the label propagation actions of the user to the unsupervised samples. We show that this procedure can significantly reduce user effort while improving the quality of the classifier on unseen test sets. Due to the limited number of supervised samples, we also propose the use of auto-encoder neural networks for feature learning. For validation, we compare the classifiers that result from the proposed approach with the ones trained from the supervised samples only and semi-supervised trained using automatic label propagation. (AU) | |
| FAPESP's process: | 16/25776-0 - Autoencoders neural networks optimization by visual analytics data |
| Grantee: | Bárbara Caroline Benato |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/25327-3 - Visual analytics for user-assisted label propagation in neural-network image classifier design |
| Grantee: | Bárbara Caroline Benato |
| Support Opportunities: | Scholarships abroad - Research Internship - Master's degree |