Evaluation of video news classification techniques for automatic content personalisation

  • Authors:

  • Marcelo G. Manzato;Alessandra A. Macedo;Rudinei Goularte

  • Affiliations:

  • Dept de Ciencias de Computacao, Instituto de Ciencias Matematicas e de Computacao (/ICMC)/, Univ de Sao Paulo (/USP)/, Campus de Sao Carlos, Sao Carlos, SP, Brazil.;Informatica Biomedica, Faculdade de Filoso&#/#/64257/a, Ciencias e Letras de Ribeirao Preto (/FFCLRP)/, Dept de Fisica e Matematica (/DFM)/, Univ de Sao Paulo (/USP)/, Mont ...;Dept de Ciencias de Computacao, Instituto de Ciencias Matematicas e de Computacao (/ICMC)/, Univ de Sao Paulo (/USP)/, Campus de Sao Carlos, Sao Carlos, SP, Brazil.

  • Venue:
  • International Journal of Advanced Media and Communication
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Personalisation tasks require the use of semantic information, extracted from multimedia streams, in order to achieve the benefits of automatic matching user preferences with multimedia content meaning. Text-based classification techniques may be used in closed-captions captured from news programmes, which can define the subject of each piece of news. Latent Semantic Indexing (LSI)-based systems are widely used for information retrieval purposes, and may be adapted to classification tasks; however, some drawbacks of the technique may impose limitations, mainly when considering multiple collections. In this paper, we compare an LSI implementation with a Genetic Algorithm (GA)-based system which was designed with the same objective. We show that the GA alternative achieves better results when used to automatically classify pieces of news video programmes.