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  • This paper presents our approach to video genre recognition which we developed for MediaEval 2011 evaluation. We treat the genre recognition task as a classification problem. We encode visual information in standard way using local features and Bag of Word representation. Audio channel is parameterized in similar way starting from its spectrogram. Further,  we exploit available automatic speech transcripts and user generated meta-data for which we compute BOW representations as well. It is reasonable to expect that semantic content of a video is strongly related to its genre, and if this semantic information was available it would make genre recognition simpler and more reliable. To this end, we used annotations for 345 semantic classes from TRECVID 2011 semantic indexing task to train semantic class detectors. Responses of these detectors were then used as features for genre recognition. The paper explains the approach in detail, it shows relative performance of the individual features a
  • This paper presents our approach to video genre recognition which we developed for MediaEval 2011 evaluation. We treat the genre recognition task as a classification problem. We encode visual information in standard way using local features and Bag of Word representation. Audio channel is parameterized in similar way starting from its spectrogram. Further,  we exploit available automatic speech transcripts and user generated meta-data for which we compute BOW representations as well. It is reasonable to expect that semantic content of a video is strongly related to its genre, and if this semantic information was available it would make genre recognition simpler and more reliable. To this end, we used annotations for 345 semantic classes from TRECVID 2011 semantic indexing task to train semantic class detectors. Responses of these detectors were then used as features for genre recognition. The paper explains the approach in detail, it shows relative performance of the individual features a (en)
Title
  • Semantic Class Detectors in Video Genre Recognition
  • Semantic Class Detectors in Video Genre Recognition (en)
skos:prefLabel
  • Semantic Class Detectors in Video Genre Recognition
  • Semantic Class Detectors in Video Genre Recognition (en)
skos:notation
  • RIV/00216305:26230/12:PU98155!RIV13-MSM-26230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7E11024), S, Z(MSM0021630528)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 167373
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/12:PU98155
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • genre recogntion, SIFT, SVM, classifier fusion, bag of words (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E65B62F3B49C]
http://linked.open...v/mistoKonaniAkce
  • Rome
http://linked.open...i/riv/mistoVydani
  • Rome
http://linked.open...i/riv/nazevZdroje
  • Proceedings of VISAPP 2012
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Řezníček, Ivo
  • Hradiš, Michal
  • Behúň, Kamil
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • SciTePress - Science and Technology Publications
https://schema.org/isbn
  • 978-989-8565-03-7
http://localhost/t...ganizacniJednotka
  • 26230
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