Name
Abstract | ||
|---|---|---|
Different events, such as terrorist acts and natural catastrophes, frequently occur across the world. The availability of images on the internet can help to understand events. However, manually selecting representative (helpful) images from a massive amount of data can be infeasible. Here, we propose an image semantic representation method that helps to understand the discrimination of Representative Images (RI) from Non-representative Images (NRI). Our method, called Event Semantic Space (ESS), generates a low-dimensional image representation by exploiting the semantics of some images with high representativeness and some representative components of the events (e.g., places, objects, and people). Results on three real-world events attest the capability of our method to represent events, outperforming three image descriptors individually in ranking tasks and presenting capability of learning patterns of Representative Images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WIFS47025.2019.9035102 | 2019 IEEE International Workshop on Information Forensics and Security (WIFS) |
Keywords | DocType | ISSN |
image semantic representation method,representative images,low-dimensional image representation,representative components,image descriptors,event understanding,Internet,nonrepresentative images,event semantic space | Conference | 2157-4766 |
ISBN | Citations | PageRank |
978-1-7281-3218-1 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caroline Mazini Rodrigues | 1 | 0 | 0.34 |
| Luís A. M. Pereira | 2 | 0 | 0.34 |
| Anderson Rocha | 3 | 913 | 69.11 |
| Zanoni Dias | 4 | 262 | 44.40 |