Semantic, automatic image annotation based on multi-layered active contours and decision trees

Joanna Isabelle Olszewska*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a new approach for automatic image annotation (AIA) in order to automatically and efficiently assign linguistic concepts to visual data such as digital images, based on both numeric and semantic features. The presented method first computes multi-layered active contours. The first-layer active contour corresponds to the main object or foreground, while the next-layers active contours delineate the object’s subparts. Then, visual features are extracted within the regions segmented by these active contours and are mapped into semantic notions. Next, decision trees are trained based on these attributes, and the image is semantically annotated using the resulting decision rules. Experiments carried out on several standards datasets have demonstrated the reliability and the computational effectiveness of our AIA system.
Original languageEnglish
Pages (from-to)201-208
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Issue number8
Publication statusPublished - 2013
Externally publishedYes


  • automatic image annotation
  • natural language tags
  • decision trees
  • semantic attributes
  • visual features
  • active contours
  • segmentation
  • image retrieval


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