Paper Info
Title
Manifold Learning For Real-World Event Understanding
Abstract
Information coming from social media is vital to the understanding of the dynamics involved in multiple events such as terrorist attacks and natural disasters. With the spread and popularization of cameras and the means to share content through social networks, an event can be followed through many different lenses and vantage points. However, social media data present numerous challenges, and frequently it is necessary a great deal of data cleaning and filtering techniques to separate what is related to the depicted event from contents otherwise useless. In a previous effort of ours, we decomposed events into representative components aiming at describing vital details of an event to characterize its defining moments. However, the lack of minimal supervision to guide the combination of representative components somehow limited the performance of the method. In this paper, we extend upon our prior work and present a learning-from-data method for dynamically learning the contribution of different components for a more effective event representation. The method relies upon just a few training samples (few-shot learning), which can be easily provided by an investigator. The obtained results on real-world datasets show the effectiveness of the proposed ideas.
Year
DOI
Venue
2021
10.1109/TIFS.2021.3070431
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Keywords
DocType
Volume
Feature extraction, Task analysis, Semantics, Social networking (online), Training, Manifolds, Manifold learning, Manifold learning, event understanding and reconstruction, image representation, image components, digital forensics
Journal
16
ISSN
Citations 
PageRank 
1556-6013
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Caroline Mazini Rodrigues100.34
Aurea Soriano-Vargas221.41
Bahram Lavi303.04
Anderson Rocha491369.11
Zanoni Dias526244.40