A relevance feedback image retrieval scheme using multi-instance and pseudo image concepts

Feng Cheng Chang*, Hsueh-Ming Hang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.

Original languageEnglish
Pages (from-to)1720-1731
Number of pages12
JournalIEICE Transactions on Information and Systems
VolumeE89-D
Issue number5
DOIs
StatePublished - 1 May 2006

Keywords

  • Image retrieval
  • Perception weighting
  • Relevance feedback

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