Content analysis of video using principal components

We use principal component analysis (PCA) to reduce the dimensionality of features of video frames for the purpose of content description. This low dimensional description makes practical the direct use of all the frames of a video sequence in later analysis. The PCA representation circumvents or eliminates several of the stumbling blocks in current analysis methods, and makes new analyses feasible. We demonstrate this with two applications. The first accomplishes high level scene description without shot detection and key frame selection. The second uses the time sequences of motion data from every frame to classify sports sequences.