Semi-Supervised Feature Embedding for Data Sanitization in Real-World Events

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 con...

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Bibliographic Details
Published inICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2495 - 2499
Main Authors Lavi, Bahram, Nascimento, Jose, Rocha, Anderson
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.06.2021
Subjects
Conferences
data relevance analysis
feature embedding
forensic application
Image sanitization
semi-supervised learning
Semisupervised learning
Signal processing
Signal processing algorithms
Social networking (online)
Supervised learning
Training
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414461