Content analysis from user's relevance feedback for content-based image retrieval

Chia Hung Wei, Chang Tsun Li

    Research output: Book chapter/Published conference paperChapter

    2 Citations (Scopus)

    Abstract

    An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user's knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user's information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user's subjective judgment. Relevance feedback serves as an information carrier to convey the user's information needs/preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.

    Original languageEnglish
    Title of host publicationArtificial Intelligence for Maximizing Content Based Image Retrieval
    PublisherIGI Global
    Pages216-234
    Number of pages19
    ISBN (Print)9781605661742
    DOIs
    Publication statusPublished - 2009

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