Video salient event classification using audio features

Silvia Corchs; Gianluigi Ciocca; Massimiliano Fiori; Francesca Gasparini

doi:10.1117/12.2039191

3 March 2014 Video salient event classification using audio features

Silvia Corchs, Gianluigi Ciocca, Massimiliano Fiori, Francesca Gasparini

Proceedings Volume 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014; 90270P (2014) https://doi.org/10.1117/12.2039191
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

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.

Citation Download Citation

Silvia Corchs, Gianluigi Ciocca, Massimiliano Fiori, and Francesca Gasparini "Video salient event classification using audio features", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270P (3 March 2014); https://doi.org/10.1117/12.2039191

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