Two-scale image retrieval with significant meta-information feedback

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

7 Scopus citations

Abstract

A two-scale image retrieval system is developed to provide efficient search in large-scale databases as well as flexibility for users to incorporate subjective preferences during retrieval. A new clustering method is developed for images each characterized by a varying number of weighted feature vectors. Furthermore, significant meta-information is mined within every cluster. A scanning mode of retrieval is created using cluster centers, which serve as a low scale version of a database in contrast to original images. In particular, users are presented with representative images of highly ranked clusters along with prominent meta-information. This retrieval approach enables users to quickly examine a large and diverse portion of a database surrounding a query and to learn about hidden connections between visual patterns and non-imagery types of data. The clusters formed also facilitate fast search in the case of individual image-based retrieval by filtering out images whose cluster centers are far from the query. The two-scale retrieval system has been implemented on a fine art painting database. Advantages of the system have been demonstrated by quantitative evaluation of the retrieval performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th ACM International Conference on Multimedia, MM 2005
Pages499-502
Number of pages4
DOIs
StatePublished - Dec 1 2005
Event13th ACM International Conference on Multimedia, MM 2005 - Singapore, Singapore
Duration: Nov 6 2005Nov 11 2005

Publication series

NameProceedings of the 13th ACM International Conference on Multimedia, MM 2005

Other

Other13th ACM International Conference on Multimedia, MM 2005
CountrySingapore
CitySingapore
Period11/6/0511/11/05

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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