Recurrent Confabulation Model for Annotated Image Retrieval

Authors:
Ryo Izawa;Naoki Motohashi;Tomohiro Takagi
Affiliations:
Department of Computer Science, Meiji University, Kanagawa, 214-8571, Japan;Department of Computer Science, Meiji University, Kanagawa, 214-8571, Japan;Department of Computer Science, Meiji University, Kanagawa, 214-8571, Japan
Venue:
International Journal of Intelligent Systems
Year:
2013

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Abstract

A text-based image retrieval system based on a brain function model, called the combination confabulation model, was developed to recognize how contextual word meanings change depending on the situation to overcome semantic gaps. In addition, a recurrent version of this system was developed to improve retrieval accuracy. The system uses a casebase that contains matched pairs of descriptions and images and searches for images that match the input queries. Experimental results showed that the proposed system exhibited more accurate image retrieval performances than conventional systems. © 2013 Wiley Periodicals, Inc.