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Proceedings Paper

Video salient event classification using audio features
Author(s): Silvia Corchs; Gianluigi Ciocca; Massimiliano Fiori; Francesca Gasparini
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Paper Abstract

The aim of this work is to detect the events in video sequences that are salient with respect to the audio signal. In particular, we focus on the audio analysis of a video, with the goal of finding which are the significant features to detect audio-salient events. In our work we have extracted the audio tracks from videos of different sport events. For each video, we have manually labeled the salient audio-events using the binary markings. On each frame, features in both time and frequency domains have been considered. These features have been used to train different classifiers: Classification and Regression Trees, Support Vector Machine, and k-Nearest Neighbor. The classification performances are reported in terms of confusion matrices.

Paper Details

Date Published: 3 March 2014
PDF: 8 pages
Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270P (3 March 2014); doi: 10.1117/12.2039191
Show Author Affiliations
Silvia Corchs, Univ. degli Studi di Milano-Bicocca (Italy)
Gianluigi Ciocca, Univ. degli Studi di Milano-Bicocca (Italy)
Massimiliano Fiori, Univ. degli Studi di Milano-Bicocca (Italy)
Francesca Gasparini, Univ. degli Studi di Milano-Bicocca (Italy)


Published in SPIE Proceedings Vol. 9027:
Imaging and Multimedia Analytics in a Web and Mobile World 2014
Qian Lin; Jan Philip Allebach; Zhigang Fan, Editor(s)

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