| Title | Semi-Supervised Feature Embedding for Data Sanitization in Real-World Events |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Lavi, Bahram, Nascimento, José, Rocha, Anderson |
| Conference Name | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Keywords | composability, compositionality, Conferences, data relevance analysis, Data Sanitization, feature embedding, forensic application, Human Behavior, Image sanitization, privacy, pubcrawl, resilience, Resiliency, semi-supervised learning, Semisupervised learning, Signal processing, Signal processing algorithms, social networking (online), supervised learning, Training |
| 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. |
| DOI | 10.1109/ICASSP39728.2021.9414461 |
| Citation Key | lavi_semi-supervised_2021 |
Semi-Supervised Feature Embedding for Data Sanitization in Real-World Events