Fisher information embedding for video indexing and retrieval
Xu Chen, Alfred O. Hero · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
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.