Date of Original Version

7-2014

Type

Conference Proceeding

Journal Title

Proceedings of IEEE International Conference on Multimedia and Expo (ICME)

First Page

1

Last Page

6

Rights Management

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Abstract or Description

In this paper, we present recent experiments on using Artificial Neural Networks (ANNs), a new “delayed” approach to speech vs. non-speech segmentation, and extraction of large-scale pooling feature (LSPF) for detecting “events” within consumer videos, using the audio channel only. A “event” is defined to be a sequence of observations in a video, that can be directly observed or inferred. Ground truth is given by a semantic description of the event, and by a number of example videos. We describe and compare several algorithmic approaches, and report results on the 2013 TRECVID Multimedia Event Detection (MED) task, using arguably the largest such research set currently available. The presented system achieved the best results in most audio-only conditions. While the overall finding is that MFCC features perform best, we find that ANN as well as LSP features provide complementary information at various levels of temporal resolution. This paper provides analysis of both low-level and high-level features, investigating their relative contributions to overall system performance.

DOI

10.1109/ICME.2014.6890234

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Published In

Proceedings of IEEE International Conference on Multimedia and Expo (ICME), 1-6.