Dimensionality reduction for distance based video clustering

Jayaraman J. Thiagarajan, Karthikeyan N. Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Clustering of video sequences is essential in order to perform video summarization. Because of the high spatial and temporal dimensions of the video data, dimensionality reduction becomes imperative before performing Euclidean distance based clustering. In this paper, we present non-adaptive dimensionality reduction approaches using random projections on the video data. Assuming the data to be a realization from a mixture of Gaussian distributions allows for further reduction in dimensionality using random projections. The performance and computational complexity of the K-means and the K-hyperline clustering algorithms are evaluated with the reduced dimensional data. Results show that random projections with an assumption of Gaussian mixtures provides the smallest number of dimensions, which leads to very low computational complexity in clustering.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence Applications and Innovations - 6th IFIP WG 12.5 International Conference, AIAI 2010, Proceedings
PublisherSpringer New York LLC
Pages270-277
Number of pages8
ISBN (Print)364216238X, 9783642162381
DOIs
StatePublished - Jan 1 2010

Publication series

NameIFIP Advances in Information and Communication Technology
Volume339 AICT
ISSN (Print)1868-4238

Keywords

  • Clustering
  • Gaussian mixtures
  • Random projections
  • Video summarization

ASJC Scopus subject areas

  • Information Systems and Management

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    Thiagarajan, J. J., Ramamurthy, K. N., & Spanias, A. (2010). Dimensionality reduction for distance based video clustering. In Artificial Intelligence Applications and Innovations - 6th IFIP WG 12.5 International Conference, AIAI 2010, Proceedings (pp. 270-277). (IFIP Advances in Information and Communication Technology; Vol. 339 AICT). Springer New York LLC. https://doi.org/10.1007/978-3-642-16239-8_36