TUDCL at MediaEval 2013 Violent Scenes Detection: Training with multi-modal features by MKL

Shinichi Goto, Terumasa Aoki

研究成果: Conference article査読

4 被引用数 (Scopus)

抄録

The purpose of this paper is to describe the work carried out for the Violent Scenes Detection task at MediaEval 2013 by team TUDCL. Our work is based on the combination of visual, temporal and audio features with machine learning at segment-level. Block-saliency-map based dense trajectory is proposed for visual and temporal features, and MFCC and delta-MFCC is used for audio features. For the classification, Multiple Kernel Learning is applied, which is effective if multi-modal features exist.

本文言語English
ジャーナルCEUR Workshop Proceedings
1043
出版ステータスPublished - 2013 1月 1
イベント2013 Multimedia Benchmark Workshop, MediaEval 2013 - Barcelona, Spain
継続期間: 2013 10月 182013 10月 19

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

フィンガープリント

「TUDCL at MediaEval 2013 Violent Scenes Detection: Training with multi-modal features by MKL」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル