| Full text | |
| Author(s): |
Camacho Presotto, Joao Gabriel
;
Valem, Lucas Pascotti
;
de Sa, Nikolas Gomes
;
Guimaraes Pedronette, Daniel Carlos
;
Papa, Joao Paulo
;
IEEE COMP SOC
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 8-pg., 2021-01-01. |
| Abstract | |
Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data. (AU) | |
| 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: | 19/11104-8 - A comparative analysis of rank correlation measures for weakly-supervised learning |
| Grantee: | Nikolas Gomes de Sá |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 19/04754-6 - Weakly supervised learning strategies through Rank-based measures |
| Grantee: | João Gabriel Camacho Presotto |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 20/11366-0 - Support for computational environments and experiments execution: weakly-supervised and classification fusion methods |
| Grantee: | Lucas Pascotti Valem |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |