Automatic adaptive metadata generation for image retrieval

Hideyasu Sasaki, Yasushi Kiyoki

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

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2005 Symposium on Applications and the Internet Workshops, SAINT2005
Pages426-429
Number of pages4
Publication statusPublished - 2005 Dec 1
Event2005 Symposium on Applications and the Internet Workshops, SAINT2005 - Trento, Italy
Duration: 2005 Jan 312005 Feb 4

Publication series

NameProceedings - 2005 Symposium on Applications and the Internet Workshops, SAINT2005
Volume2005

Other

Other2005 Symposium on Applications and the Internet Workshops, SAINT2005
CountryItaly
CityTrento
Period05/1/3105/2/4

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Sasaki, H., & Kiyoki, Y. (2005). Automatic adaptive metadata generation for image retrieval. In Proceedings - 2005 Symposium on Applications and the Internet Workshops, SAINT2005 (pp. 426-429). [1620065] (Proceedings - 2005 Symposium on Applications and the Internet Workshops, SAINT2005; Vol. 2005).