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 journalConference articlepeer-review

    1 Scopus citations


    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 image characteristics from the multiple positive samples provided by the user. The second key concept is that when the user does not provide a sufficient number of samples, how we generate a set of consistent "pseudo images". 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
    Article number23
    Pages (from-to)224-235
    Number of pages12
    JournalProceedings of SPIE - The International Society for Optical Engineering
    StatePublished - 21 Jul 2005
    EventProceedings of SPIE-IS and T Electronic Imaging - Storage and Retrieval Methods for Multimedia 2005 - San Jose, CA, United States
    Duration: 18 Jan 200519 Jan 2005


    • Image retrieval
    • Perception weighting


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