Image annotations by combining multiple evidence & WordNet

Yohan Jin, Latifur Khan, Lei Wang, Mamoun Awad

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

232 Citations (Scopus)


The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.

Original languageEnglish
Title of host publicationProceedings of the 13th ACM International Conference on Multimedia, MM 2005
Number of pages10
Publication statusPublished - 2005
Externally publishedYes
Event13th ACM International Conference on Multimedia, MM 2005 - Singapore, Singapore
Duration: Nov 6 2005Nov 11 2005

Publication series

NameProceedings of the 13th ACM International Conference on Multimedia, MM 2005


Other13th ACM International Conference on Multimedia, MM 2005


  • Corel Dataset
  • Dempster-Shafer Rule
  • Image Annotation
  • Management
  • Semantic-Similarity
  • WordNet

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software


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