Group Feature Selection for Audio-based Video Genre Classification

Group Feature Selection for Audio-based Video Genre Classification

Abstract

The performance of video genre classification approaches strongly depends on the selected feature set. Feature selection requires for expert knowledge and is commonly driven by the underlying data, investigated video genres, and previous experience in related application scenarios. An alteration of the genres of interest results in reconsideration of the employed features by an expert. In this work, we introduce an unsupervised method for the selection of features that efficiently represent the underlying data. Performed experiments in the context of audio-based video genre classification demonstrate the outstanding performance of the proposed approach and its robustness across different video datasets and genres.

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Authors
  • Sageder, Gerhard
  • Zaharieva, Maia
  • Breiteneder, Christian
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Full Paper in Proceedings)
Event Title
The 22nd International Conference on Multimedia Modelling (MMM 2016)
Divisions
Multimedia Information Systems
Subjects
Multimedia
Event Location
Miami, USA
Event Type
Conference
Event Dates
4-6 January, 2016
Date
January 2016
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