Similarity search for videos based on robust latent semantic analysis

Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review


    A method retrieving videos is presented by utilizing vector quantization and latent semantic analysis. Each video is represented by a sequence of signatures through the vector quantization of frame datasets. Latent semantic analysis is then applied to the signature with a video matrix. We verified through experiments that dimensionality reduction in latent semantic analysis increases the speed and precision of retrieval. Making vector quantization more robust further improved the performance of similarity searches.

    Original languageEnglish
    Pages (from-to)1835-1838
    Number of pages4
    JournalKyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
    Issue number12
    Publication statusPublished - Dec 2004

    All Science Journal Classification (ASJC) codes

    • Media Technology
    • Computer Science Applications
    • Electrical and Electronic Engineering


    Dive into the research topics of 'Similarity search for videos based on robust latent semantic analysis'. Together they form a unique fingerprint.

    Cite this