. "Rome" . . "Rome" . "Semantic Class Detectors in Video Genre Recognition"@en . . "3"^^ . . "7"^^ . . . . "Semantic Class Detectors in Video Genre Recognition" . "Proceedings of VISAPP 2012" . . . . "SciTePress - Science and Technology Publications" . . . . "978-989-8565-03-7" . "26230" . "P(7E11024), S, Z(MSM0021630528)" . "RIV/00216305:26230/12:PU98155" . "Semantic Class Detectors in Video Genre Recognition"@en . "Beh\u00FA\u0148, Kamil" . . "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,\u00A0 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 . . "Hradi\u0161, Michal" . . . "Semantic Class Detectors in Video Genre Recognition" . . "[E65B62F3B49C]" . . "Beh\u00FA\u0148, Kamil" . "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,\u00A0 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" . . "RIV/00216305:26230/12:PU98155!RIV13-MSM-26230___" . . "genre recogntion, SIFT, SVM, classifier fusion, bag of words"@en . . "167373" . "3"^^ . "2012-02-24+01:00"^^ . "\u0158ezn\u00ED\u010Dek, Ivo" . .