Smart Multimedia Information Retrieval

Stefan Wagenpfeil, Paul McKevitt, Matthias Hemmje

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    The area of multimedia information retrieval (MMIR) faces two major challenges: the enormously growing number of multimedia objects (i.e., images, videos, audio, and text files), and the fast increasing level of detail of these objects (e.g., the number of pixels in images). Both challenges lead to a high demand of scalability, semantic representations, and explainability of MMIR processes. Smart MMIR solves these challenges by employing graph codes as an indexing structure, attaching semantic annotations for explainability, and employing application profiling for scaling, which results in human-understandable, expressive, and interoperable MMIR. The mathematical foundation, the modeling, implementation detail, and experimental results are shown in this paper, which confirm that Smart MMIR improves MMIR in the area of efficiency, effectiveness, and human understandability.
    Original languageEnglish
    Pages (from-to)198-224
    Number of pages27
    JournalAnalytics
    Volume2
    Issue number1
    Early online date20 Feb 2023
    DOIs
    Publication statusPublished online - 20 Feb 2023

    Keywords

    • indexing
    • retrieval
    • explainability
    • semantic
    • multimedia
    • feature graph
    • graph code
    • information retrieval

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