We cluster information in a video by similarity, and sort them by occurrence frequency and by temporal variation frequency. Temporal invariants are defined as information with temporal variation frequencies are substantially zero. These invariants are shared along temporal dimension of videos for data compression, information gaining. We judge some of the invariants as background. Clustering also create structure in a videos. These clusters can be recognized from named objects, patterned relationship database and landscape database. With these recognized names and patterns, the structure of video can be serialized into natural language. Background from multiple videos can then be merged and concatenated into large continuous backgrounds. Large quantity of outdoor video can form a landscape database with moving objects and people been removed. Overlapping background from different time allows for high variant portion of the background been identified for Lost and Found and intelligence gathering application. Viewing point and viewing angle normalization, multiple resolution matching (and recoding) biased toward lower resolution preceding higher resolution are important technique to vastly improving opportunities of finding good matching. Viewing point and viewing angle normalization produce a set of discrete viewing points and viewing angles, where information from different viewing point and viewing angles are further shared.
Video Factorization By Temporal Stability And Spatial Resolutions III