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


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