loading...
Automatic Adaptive Metadata Generation for Image Retrieval
Trento, Italy January 31-February 04
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SAINTW.2005.392005 Symposium on Applications and th ...
 This Article 
 
PDF
HTML
IEEE Xplore Subscribers
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Hideyasu Sasaki, Keio University
Yasushi Kiyoki, Keio University
In this paper, we present an automatic adaptive metadata generation system using content analysis of sample images. First, our system screens out improper query images for metadata generation by using CBIR that computes structural similarity between sample images and query images. Second, the system generates metadata by selecting sample indexes attached to the sample images that are structurally similar to query images. Third, the system detects improper metadata and re-generates proper metadata by identifying wrongly selected metadata. Our system has improved metadata generation by 23.5% on recall ratio and 37% on fallout ratio rather than just using the results of content analysis.
Citation:
Hideyasu Sasaki, Yasushi Kiyoki, "Automatic Adaptive Metadata Generation for Image Retrieval," saint-w, pp.426-429, 2005 Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops), 2005
Usage of this product signifies your acceptance of the Terms of Use.