Multimedia Tools and Applications

, Volume 75, Issue 15, pp 9045–9072

A modified vector of locally aggregated descriptors approach for fast video classification

  • Ionuţ Mironică
  • Ionuţ Cosmin Duţă
  • Bogdan Ionescu
  • Nicu Sebe
Article

DOI: 10.1007/s11042-015-2819-7

Cite this article as:
Mironică, I., Duţă, I.C., Ionescu, B. et al. Multimed Tools Appl (2016) 75: 9045. doi:10.1007/s11042-015-2819-7

Abstract

In order to reduce the computational complexity, most of the video classification approaches represent video data at frame level. In this paper we investigate a novel perspective that combines frame features to create a global descriptor. The main contributions are: (i) a fast algorithm to densely extract global frame features which are easier and faster to compute than spatio-temporal local features; (ii) replacing the traditional k-means visual vocabulary from Bag-of-Words with a Random Forest approach allowing a significant speedup; (iii) the use of a modified Vector of Locally Aggregated Descriptor(VLAD) combined with a Fisher kernel approach that replace the classic Bag-of-Words approach, allowing us to achieve high accuracy. By doing so, the proposed approach combines the frame-based features effectively capturing video content variation in time. We show that our framework is highly general and is not dependent on a particular type of descriptors. Experiments performed on four different scenarios: movie genre classification, human action recognition, daily activity recognition and violence scene classification, show the superiority of the proposed approach compared to the state of the art.

Keywords

Capturing content variation in time in video Modified vector of locally aggregated descriptor Random forests Video classification 

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ionuţ Mironică
    • 1
  • Ionuţ Cosmin Duţă
    • 2
  • Bogdan Ionescu
    • 1
  • Nicu Sebe
    • 2
  1. 1.LAPIUniversity Politehnica of BucharestBucharestRomania
  2. 2.DISIUniversity of TrentoTrentoItaly

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