Automatic Adaptive Metadata Generation for Image Retrieval

  • Authors:

  • Hideyasu Sasaki;Yasushi Kiyoki

  • Affiliations:

  • Keio University;Keio University

  • Venue:
  • SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
  • Year:
  • 2005

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Abstract

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.