A feature-word-topic model for image annotation

Cam Tu Nguyen, Natsuda Kaothanthong, Xuan Hieu Phan, Takeshi Tokuyama

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

9 Citations (Scopus)

Abstract

Image annotation is to automatically associate semantic labels with images in order to obtain a more convenient way for indexing and searching images on the Web. This paper proposes a novel method for image annotation based on feature-word and word-topic distributions. The introduction of topics allows us to take word associations, such as {ocean, fish, coral}, into image annotation in an efficient way. Feature-word distributions are utilized to define weights in computation of topic distributions for annotation. By doing so, topic models in text mining can be applied directly in our method. Experiments show that our method is able to obtain promising improvements over the state-of-the-art method - Supervised Multiclass Labeling (SML).

Original languageEnglish
Title of host publicationCIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
Pages1481-1484
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 1
Event19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada
Duration: 2010 Oct 262010 Oct 30

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
CountryCanada
CityToronto, ON
Period10/10/2610/10/30

Keywords

  • Image annotation
  • Mixture hierarchies
  • Topic models

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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