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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.