Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Humberto Evans

dblp:37/4904 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › indexing
content-based indexing
0.112007
Unsupervised content-based indexing for sports video retrieval · ACM Multimedia 2007
Multimedia analysis and retrieval › sports video analysis
sports video retrieval
0.112007
Unsupervised content-based indexing for sports video retrieval · ACM Multimedia 2007
Multimedia analysis and retrieval
video retrieval
0.112007
Unsupervised content-based indexing for sports video retrieval · ACM Multimedia 2007
Information retrieval › text analysis
Natural language processing for IR
0.012007
Unsupervised content-based indexing for sports video retrieval · ACM Multimedia 2007

Methods — techniques the papers use, named apart from their topics

unsupervised learning · 0.1grounded language model · 0.1
YearPublicationVenuePosition
2007 Unsupervised content-based indexing for sports video retrieval
abstract
This demonstration presents an interface to a corpus of broadcast baseball games that have been indexed using an unsupervised content-based method introduced here. The method uses the concept of a grounded language model to motivate a framework in which video is searched using natural language with no reliance on predetermined concepts or hand labeled events. The interface demonstrates the effectiveness of the technique and the ease of use it affords the user.
Michael Fleischman, Humberto Evans, Deb Roy
ACM Multimedia2