Sorry, you need to enable JavaScript to visit this website.

SEMI-SUPERVISED FEATURE EMBEDDING FOR DATA SANITIZATION IN REAL-WORLD EVENTS

Primary tabs

Citation Author(s):
José Nascimento, Anderson Rocha
Submitted by:
bahram lavi
Last updated:
27 June 2021 - 8:55am
Document Type:
Research Manuscript
Document Year:
2021
Event:
Presenters Name:
Bahram Lavi
Paper Code:
1546

Abstract 

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.

up
1 user has voted: bahram lavi

Dataset Files

Manuscript

(27)

Slides

(34)