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7 February 2011 Fisher information embedding for video indexing and retrieval
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Proceedings Volume 7873, Computational Imaging IX; 78730A (2011) https://doi.org/10.1117/12.874036
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xu Chen and Alfred O. Hero "Fisher information embedding for video indexing and retrieval", Proc. SPIE 7873, Computational Imaging IX, 78730A (7 February 2011); https://doi.org/10.1117/12.874036
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