Demo Data from EDBT 2026 / reviewers in the wild / expert
Igor Calil Loures de Oliveira
dblp:76/10574
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › multimedia analysis
multimodal video analysis |
0.1 | 1 | 2011 | News browsing system: multimodal analysis · ACM Multimedia 2011 |
Multimedia analysis and retrieval › multimedia browsing
video browsing |
0.1 | 1 | 2011 | News browsing system: multimodal analysis · ACM Multimedia 2011 |
Multimedia analysis and retrieval
video summarization |
0.1 | 1 | 2011 | News browsing system: multimodal analysis · ACM Multimedia 2011 |
Information retrieval
keyword search |
0.0 | 1 | 2011 | News browsing system: multimodal analysis · ACM Multimedia 2011 |
Information retrieval › document retrieval
text search |
0.0 | 1 | 2011 | News browsing system: multimodal analysis · ACM Multimedia 2011 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.2face detection · 0.2audio transcription · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | News browsing system: multimodal analysisabstractThis paper reports a system developed for video browsing based on multimodal analysis. Our multimodal approach performs audio transcription for shot categorization (sports, weather, politics and economy) combining audio and visual information for theme categorization. Its main features include static and dynamic summaries, segmentation using face detection, classification into Indoor/Outdoor scenes based on Support Vector Machine (SVM) and audio transcription for theme keyword search. Keywords are selected to represent the subjects, followed by a simple text search. We conduct a set of experiments for evaluating the effectiveness of the shot subject categorization using audio transcription information. Bruno do Nascimento Teixeira, Júlia Epischina Engrácia de Oliveira, Fillipe Dias Moreira de Souza, Tiago Oliveira Cunha, Arnaldo de Albuquerque Araújo, Christiane Okamoto, Lucas Figueiredo, Vinícius de Oliveira Silva, Igor Calil Loures de Oliveira |
ACM Multimedia | 9 |