A feature-word-topic model for image annotation

Authors:
Cam-Tu Nguyen;Natsuda Kaothanthong;Xuan-Hieu Phan;Takeshi Tokuyama
Affiliations:
Tohoku University, Sendai, Japan;Tohoku University, Sendai, Japan;University of New South Wales, Sydney, Australia;Tohoku University, Sendai, Japan
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

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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 enables us to efficiently take word associations, such as {ocean, fish, coral}, into image annotation. 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)