Correlation-based video semantic concept detection using multiple correspondence analysis

Lin Lin, Guy Ravitz, Mei Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

Abstract

Semantic concept detection has emerged as an intriguing topic in multimedia research recently. The ability to interpret high-level semantics from low-level features has been the long desired goal of many researchers. In this paper, we propose a novel framework that utilizes the ability of multiple correspondence analysis (MCA) to explore the correlation between different items (feature-value pairs) and classes (concepts) to bridge the gap between the extracted low-level features and high-level semantic concepts. Using the concepts and benchmark data identified and provided by the TRECVID project, we have shown that our proposed framework demonstrates promising results and performs better than the Decision Tree (DT), Support Vector Machine (SVM), and Naive Bayesian (NB) classifiers that are commonly applied to the TRECVID datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Symposium on Multimedia, ISM 2008
Pages316-321
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event10th IEEE International Symposium on Multimedia, ISM 2008 - Berkeley, CA, United States
Duration: Dec 15 2008Dec 17 2008

Publication series

NameProceedings - 10th IEEE International Symposium on Multimedia, ISM 2008

Other

Other10th IEEE International Symposium on Multimedia, ISM 2008
CountryUnited States
CityBerkeley, CA
Period12/15/0812/17/08

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Electrical and Electronic Engineering

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