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Igor Calil Loures de Oliveira

dblp:76/10574 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › multimedia analysis
multimodal video analysis
0.112011
News browsing system: multimodal analysis · ACM Multimedia 2011
Multimedia analysis and retrieval › multimedia browsing
video browsing
0.112011
News browsing system: multimodal analysis · ACM Multimedia 2011
Multimedia analysis and retrieval
video summarization
0.112011
News browsing system: multimodal analysis · ACM Multimedia 2011
Information retrieval
keyword search
0.012011
News browsing system: multimodal analysis · ACM Multimedia 2011
Information retrieval › document retrieval
text search
0.012011
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
YearPublicationVenuePosition
2011 News browsing system: multimodal analysis
abstract
This 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 Multimedia9