Multimedia event detection with ℓ2-regularized logistic Gaussian mixture regression

Changyu Liu, Shoubin Dong, Bin Lu, Mohamed Abdel-Mottaleb

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Multimedia event detection (MED) is one of the most important branches of multimedia content analysis. Current research work on MED focuses mainly on detecting specific events, such as sport events, news events and suspicious events, which is far from achieving a complicated and generic MED due to the fact that these events usually contain a lot of visual attributes, such as objects, scenes and human actions. Being different from visual features, visual attributes are hidden classes to event detectors and event classifiers. Hence, proper representation of these visual attributes could be helpful in building a sophisticated and generic MED. In this paper, we use Gaussian mixture model (GMM) for representing video events with the motivation that the individual component densities of GMM could model some underlying hidden visual attributes and propose a ℓ2-regularized logistic Gaussian mixture regression approach, which is also called LLGMM classifier, for a more generic and complicated MED. We also propose an efficient iterative algorithm, which uses gradient descent, a standard convex optimization method, to solve the objective function of LLGMM. Finally, extensive experiments are conducted on the challenging TRECVID MED 2012 development dataset. The results demonstrate the effectiveness of the proposed LLGMM classifier for MED.

Original languageEnglish (US)
Pages (from-to)1561-1574
Number of pages14
JournalNeural Computing and Applications
Volume26
Issue number7
DOIs
StatePublished - Oct 21 2015

Keywords

  • Gaussian mixture model
  • LLGMM classifier
  • Logistic regression
  • Multimedia event detection
  • ℓ Regularization

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Multimedia event detection with ℓ<sub>2</sub>-regularized logistic Gaussian mixture regression'. Together they form a unique fingerprint.

  • Cite this