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Proceedings Paper

Fisher information embedding for video indexing and retrieval
Author(s): Xu Chen; Alfred O. Hero
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Paper Abstract

In this paper, we present a novel information embedding based approach for video indexing and retrieval. The high dimensionality for video sequences still poses a major challenge of video indexing and retrieval. Different from the traditional dimensionality reduction techniques such as Principal Component Analysis (PCA), we embed the video data into a low dimensional statistical manifold obtained by applying manifold learning techniques to the information geometry of video feature probability distributions (PDF). We estimate the PDF of the video features using histogram estimation and Gaussian mixture models (GMM), respectively. By calculating the similarities between the embedded trajectories, we demonstrate that the proposed approach outperforms traditional approaches to video indexing and retrieval with real world data.

Paper Details

Date Published: 7 February 2011
PDF: 7 pages
Proc. SPIE 7873, Computational Imaging IX, 78730A (7 February 2011); doi: 10.1117/12.874036
Show Author Affiliations
Xu Chen, Univ. of Michigan, Ann Arbor (United States)
Alfred O. Hero, Univ. of Michigan, Ann Arbor (United States)

Published in SPIE Proceedings Vol. 7873:
Computational Imaging IX
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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