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    Content-based image search system design for capturing user preferences during query formulation

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    Authors
    Artemi, Mahmoud
    Liu, Haiming
    Affiliation
    University of Bedfordshire
    Issue Date
    2020-07-30
    Subjects
    relevance feedback
    Vakkari’s three-stage model
    query formulation
    interactive machine learning
    content-based image retrieval
    user interface
    
    Metadata
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    Abstract
    Most existing studies of content-based image retrieval (CBIR) system design focus on learning users’ information needs through relevance feedback at the result assessment stage only. However, in many CBIR systems, the underlying machine learning mechanisms need the users’ feedback at query formulation stage for a better training and search performance, which unfortunately is often not supported by the search interface design. The lack of support for the users’ query formulation through an effective CBIR interface has been a drawback for system performance and the users’ search satisfaction and experiences. We propose a new CBIR system design approach based on Vakkari’s three-stage model, which encourages the users to provide feedback at the query formulation stage through a user-centered interface. The interface helps the users to form and express their information needs through enabling the users to participate in the training phase of the machine learning mechanism of the system. A user study with 28 participants shows how the proposed system design supports the users’ interaction through the user-centered search interface. The findings of this study highlight the importance for the users to engage in all stages of the search process, especially at the query formulation stage when the considered mechanism requires a training process, through a user-centered interaction design.
    Citation
    Artemi M, Liu H (2020) 'Content-based image search system design for capturing user preferences during query formulation', 1st Workshop on Bridging the Gap between Information Science, Information Retrieval and Data Science - Xi'an, CEUR-WS.
    Publisher
    CEUR-WS
    URI
    http://hdl.handle.net/10547/624773
    Additional Links
    https://birds-ws.github.io/birds2020/assets/papers/BIRDS2020_artemi.pdf
    Type
    Conference papers, meetings and proceedings
    Language
    en
    Collections
    Computing

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