Effective semantic annotation by image-to-concept distribution model

Ja Hwung Su*, Chien Li Chou, Ching Yung Lin, S. Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Image annotation based on visual features has been a difficult problem due to the diverse associations that exist between visual features and human concepts. In this paper, we propose a novel approach called Annotation by Image-to-Concept Distribution Model (AICDM) for image annotation by discovering the associations between visual features and human concepts from image-to-concept distribution. Through the proposed image-to-concept distribution model, visual features and concepts can be bridged to achieve high-quality image annotation. In this paper, we propose to use visual features, models, and visual genes which represent analogous functions to the biological chromosome, DNA, and gene. Based on the proposed models using entropy, tf-idf, rules, and SVM, the goal of high-quality image annotation can be achieved effectively. Our empirical evaluation results reveal that the AICDM method can effectively alleviate the problem of visual-to-concept diversity and achieve better annotation results than many existing state-of-the-art approaches in terms of precision and recall.

Original languageEnglish
Article number5733423
Pages (from-to)530-538
Number of pages9
JournalIEEE Transactions on Multimedia
Issue number3
StatePublished - 1 Jun 2011


  • Entropy
  • image annotation
  • image-to-concept distribution
  • tf-idf

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