| Full text | |
| Author(s): |
Lavi, Bahram
;
Nascimento, Jose
;
Rocha, Anderson
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021); v. N/A, p. 5-pg., 2021-01-01. |
| Abstract | |
With the rapid growth of data sharing through social media networks, determining relevant data items concerning a particular subject becomes paramount. We address the issue of establishing which images represent an event of interest through a semi-supervised learning technique. The method learns consistent and shared features related to an event (from a small set of examples) to propagate them to an unlabeled set. We investigate the behavior of five image feature representations considering low- and high-level features and their combinations. We evaluate the effectiveness of the feature embedding approach on five collected datasets from real-world events. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 20/02241-9 - Pattern discovery and event highlight from heterogenous sources |
| Grantee: | José Dorivaldo Nascimento Souza Júnior |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 18/05668-3 - Feature-space-time Coherence with Heterogeneous Data |
| Grantee: | Bahram Lavi Sefidgari |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |