Correlation-based interestingness measure for video semantic concept detection

Lin Lin, Mei Ling Shyu, Shu Ching Chen

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

4 Scopus citations

Abstract

The technique of performing classification using association rule mining (ARM) has been adopted to bridge the multimedia semantic gap between low-level features and high-level concepts of interest, taking advantages of both classification and association rule mining. One of the most important research approaches in ARM is to investigate the interestingness measure which plays a key role in association rule discovery stage and rule selection stage. In this paper, a new correlation-based interestingness measure that is used at both stages is proposed. The association rules are generated by a novel interestingness measure obtained from applying multiple correspondence analysis (MCA) to explore the correlation between two feature-value pairs and concept classes. Then the correlation-based interestingness measure is reused and aggregated with the inter-similarity and intra-similarity values to rank the final rule set for classification. Detecting the concepts from the benchmark data provided by the TRECVID project, we have shown that our proposed framework achieves higher accuracy than the classifiers that are commonly applied to multimedia retrieval.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Pages120-125
Number of pages6
DOIs
StatePublished - Nov 17 2009
Event2009 IEEE International Conference on Information Reuse and Integration, IRI 2009 - Las Vegas, NV, United States
Duration: Aug 10 2009Aug 12 2009

Publication series

Name2009 IEEE International Conference on Information Reuse and Integration, IRI 2009

Other

Other2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
CountryUnited States
CityLas Vegas, NV
Period8/10/098/12/09

Keywords

  • Interestingness measure
  • Multiple correspondence analysis
  • Semantic concept detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

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  • Cite this

    Lin, L., Shyu, M. L., & Chen, S. C. (2009). Correlation-based interestingness measure for video semantic concept detection. In 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009 (pp. 120-125). [5211537] (2009 IEEE International Conference on Information Reuse and Integration, IRI 2009). https://doi.org/10.1109/IRI.2009.5211537