A time series data dimensional compression apparatus performing dimensional compression for improving the efficiency of searching for time series data without losing the features of data. The compression is made to a determined dimension so that a larger volume of information may be extracted therein. A time series subsequence generating section (112) generates time series subsequences of a specified segment width into which a plurality of pieces of time series data generated at a time series data generating section (110) are divided. A singular value decomposition processing section (113) performs singular value decomposition on all of the time series subsequences. A dimensional compression time series data generating section (114) generates dimensional compression time series data by using high-order elements of the singular value decomposition as a representative value of the time series subsequence.

A method summarizes unknown content of a video. First, low-level features of the video are selected. The video is then partitioned into segments according to the low-level features. The segments are grouped into disjoint clusters where each cluster contains similar segments. The clusters are labeled according to the low-level features, and parameters characterizing the clusters are assigned. High-level patterns among the labels are found, and the these patterns are used to extract frames from the video according to form a content-adaptive summary of the unknown content of the video.

A method mines unknown content of a video by first selecting one or more low-level features of the video. For each selected feature, or combination of features, time series data is generated. The time series data is then self-correlated to identify similar segments of the video according to the low-level features. The similar segments are grouped into clusters to discover high-level patterns in the unknown content of video.