Multimedia Tools and Applications

, Volume 76, Issue 5, pp 7041–7065

Evaluation of multiple features for violent scenes detection

  • Vu Lam
  • Sang Phan
  • Duy-Dinh Le
  • Duc Anh Duong
  • Shin’ichi Satoh
Article

DOI: 10.1007/s11042-016-3331-4

Cite this article as:
Lam, V., Phan, S., Le, DD. et al. Multimed Tools Appl (2017) 76: 7041. doi:10.1007/s11042-016-3331-4

Abstract

Violent scenes detection (VSD) is a challenging problem because of the heterogeneous content, large variations in video quality, and complex semantic meanings of the concepts involved. In the last few years, combining multiple features from multi-modalities has proven to be an effective strategy for general multimedia event detection (MED), but the specific event detection like VSD has been comparatively less studied. Here, we evaluated the use of multiple features and their combination in a violent scenes detection system. We rigorously analyzed a set of low-level features and a deep learning feature that captures the appearance, color, texture, motion and audio in video. We also evaluated the utility of mid-level visual information obtained from detecting related violent concepts. Experiments were performed on the publicly available MediaEval VSD 2014 dataset. The results showed that visual and motion features are better than audio features. Moreover, the performance of the mid-level features was nearly as good as that of the low-level visual features. Experiments with a number of fusion methods showed that all single features are complementary and help to improve overall performance. This study also provides an empirical foundation for selecting feature sets that are capable of dealing with heterogeneous content comprising violent scenes in movies.

Keywords

Violent scenes detection Video retrieval Multi-modal fusion Multiple features 

Funding information

Funder NameGrant NumberFunding Note
Vietnam National University Ho Chi Minh City (VNU-HCM) Grant
  • B2013-26-01

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Vu Lam
    • 1
  • Sang Phan
    • 2
  • Duy-Dinh Le
    • 2
  • Duc Anh Duong
    • 3
  • Shin’ichi Satoh
    • 2
  1. 1.University of Science, VNU-HCMCHo Chi MinhVietnam
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.University of Information Technology, VNU-HCMCHo Chi MinhVietnam

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