Detail publikačního výsledku

Brno University of Technology at MediaEval 2011 Genre Tagging Task

HRADIŠ, M.; ŘEZNÍČEK, I.; BEHÚŇ, K.

Originální název

Brno University of Technology at MediaEval 2011 Genre Tagging Task

Anglický název

Brno University of Technology at MediaEval 2011 Genre Tagging Task

Druh

Stať ve sborníku mimo WoS a Scopus

Originální abstrakt

This paper briefly describes our approach to the video genre tagging task which was a part of MediaEval 2011. We focused mainly on visual and audio information, and we exploited metadata and automatic speech transcripts
only in a very basic way. Our approach relied on classification and on classifier fusion to combine
different sources of information. We did not use any additional training data except the very small
exemplary set provided by MediaEval (only 246 videos). The best performance was achieved by metadata alone.
Combination with the other sources of information did not improve results in the submitted runs. This was achieved
later by choosing more suitable weights in fusion. Excluding the metadata,
audio and video gave better results than speech transcripts. Using classifiers for 345 semantic classes
from TRECVID 2011 semantic indexing (SIN) task to project the data worked better than classifying directly from video and audio features.

Anglický abstrakt

This paper briefly describes our approach to the video genre tagging task which was a part of MediaEval 2011. We focused mainly on visual and audio information, and we exploited metadata and automatic speech transcripts
only in a very basic way. Our approach relied on classification and on classifier fusion to combine
different sources of information. We did not use any additional training data except the very small
exemplary set provided by MediaEval (only 246 videos). The best performance was achieved by metadata alone.
Combination with the other sources of information did not improve results in the submitted runs. This was achieved
later by choosing more suitable weights in fusion. Excluding the metadata,
audio and video gave better results than speech transcripts. Using classifiers for 345 semantic classes
from TRECVID 2011 semantic indexing (SIN) task to project the data worked better than classifying directly from video and audio features.

Klíčová slova

genre recognition, bag of words, SIFT, local features, SVM, classification, classifier fusion

Klíčová slova v angličtině

genre recognition, bag of words, SIFT, local features, SVM, classification, classifier fusion

Autoři

HRADIŠ, M.; ŘEZNÍČEK, I.; BEHÚŇ, K.

Rok RIV

2016

Vydáno

01.09.2011

Nakladatel

CEUR-WS.org

Místo

Pisa, Italy

Kniha

Working Notes Proceedings of the MediaEval 2011 Workshop

ISSN

1613-0073

Periodikum

CEUR Workshop Proceedings

Číslo

9

Stát

Spolková republika Německo

Strany od

1

Strany do

2

Strany počet

2

URL

http://ceur-ws.org/Vol-807/Hradis_BUT_Genre_me11wn.pdf

BibTex

@inproceedings{BUT91115,
  author="Michal {Hradiš} and Ivo {Řezníček} and Kamil {Behúň}",
  title="Brno University of Technology at MediaEval 2011 Genre Tagging Task",
  booktitle="Working Notes Proceedings of the MediaEval 2011 Workshop",
  year="2011",
  journal="CEUR Workshop Proceedings",
  number="9",
  pages="1--2",
  publisher="CEUR-WS.org",
  address="Pisa, Italy",
  issn="1613-0073",
  url="http://ceur-ws.org/Vol-807/Hradis_BUT_Genre_me11wn.pdf"
}