Utilizing Indirect Associations in Multimedia Semantic Retrieval

Hsin Yu Ha, Shu Ching Chen, Mei Ling Shyu

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

4 Scopus citations


Technological developments have lead to the propagation of massive amounts of data in the form of text, image, audio, and video. The unstoppable trend draws researchers' attention to develop approaches to efficiently retrieve and manage multimedia data. The inadequacy of keyword-based search in multimedia data retrieval due to non-existent or incomplete text annotations has called for the development of a contentbased multimedia data management framework. Specifically, detecting high-level semantic concepts is one of the rapidly growing topics in this regard. In order to thoroughly identify semantic concepts in data which have different representations and are derived from different modalities, both positive and negative inter-concept correlations have been recently studied and explored to enhance the re-ranking performance. In this paper, an indirect association rule mining (IARM) approach is introduced to reveal the hidden correlation among semantic concepts. The effectiveness of IARM is evaluated by Multiple Correspondence Analysis (MCA). Furthermore, normalization and score integration are performed to achieve the optimal classification results. The TRECVID 2011 benchmark dataset is used to show the effectiveness of the proposed IARM factor in the re-ranking process.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479986880
StatePublished - Jul 9 2015
Event1st IEEE International Conference on Multimedia Big Data, BigMM 2015 - Beijing, China
Duration: Apr 20 2015Apr 22 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015


Other1st IEEE International Conference on Multimedia Big Data, BigMM 2015


  • Concept Mining
  • Indirect Association Rule Mining (IARM)
  • Multimedia Data
  • Re-ranking
  • Semantic Concept Detection

ASJC Scopus subject areas

  • Information Systems
  • Media Technology


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