An efficient clustering and indexing approach over large video sequences

Yu Yang, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In a video database, the similarity between video sequences is usually measured by the percentages of similar frames shared by both video sequences, where each frame is represented as a high-dimensional feature vector. The direct computation of the similarity measure involves time-consuming sequential scans over the whole dataset. On the other hand, adopting existing indexing technique to high-dimensional datasets suffers from the "Dimensionality Curse". Thus, an efficient and effective indexing method is needed to reduce the computation cost for the similarity search. In this paper, we propose a Multi-level Hierarchical Divisive Dimensionality Reduction technique to discover correlated clusters, and develop a corresponding indexing structure to efficiently index the clusters in order to support efficient similarity search over video data. By using dimensionality reduction techniques as Principal Component Analysis, we can restore the critical information between the data points in the dataset using a reduced dimension space. Experiments show the efficiency and usefulness of this approach.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2006
Subtitle of host publication7th Pacific Rim Conference on Multimedia, Proceedings
Number of pages10
ISBN (Print)3540487662, 9783540487661
Publication statusPublished - 1 Jan 2006
Externally publishedYes
EventPCM 2006: 7th Pacific Rim Conference on Multimedia - Hangzhou, China
Duration: 2 Nov 20064 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4261 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePCM 2006: 7th Pacific Rim Conference on Multimedia

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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