A semantic framework for video genre classification and event analysis

  • Authors:

  • Junyong You;Guizhong Liu;Andrew Perkis

  • Affiliations:

  • Centre for Quantifiable Quality of Service in Communication Systems1Centre for Quantifiable Quality of Service in Communication Systems, Center of Excellence, appointed by The Research Council of ...;School of Electronic and Information Engineering, Xi'an Jiaotong University, China;Centre for Quantifiable Quality of Service in Communication Systems1Centre for Quantifiable Quality of Service in Communication Systems, Center of Excellence, appointed by The Research Council of ...

  • Venue:
  • Image Communication
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Semantic video analysis is a key issue in digital video applications, including video retrieval, annotation, and management. Most existing work on semantic video analysis is mainly focused on event detection for specific video genres, while the genre classification is treated as another independent issue. In this paper, we present a semantic framework for weakly supervised video genre classification and event analysis jointly by using probabilistic models for MPEG video streams. Several computable semantic features that can accurately reflect the event attributes are derived. Based on an intensive analysis on the connection between video genres and the contextual relationship among events, as well as the statistical characteristics of dominant event, a hidden Markov model (HMM) and naive Bayesian classifier (NBC) based analysis algorithm is proposed for video genre classification. Another Gaussian mixture model (GMM) is built to detect the contained events using the same semantic features, whilst an event adjustment strategy is proposed according to an analysis on the GMM structure and pre-definition of video events. Subsequently, a special event is recognized based on the detected events by another HMM. The simulative experiments on video genre classification and event analysis using a large number of video data sets demonstrate the promising performance of the proposed framework for semantic video analysis.