Temporal structure methods for image-based change analysis

– This paper addresses the exploitation of massive numbers of image-derived change detections. We use the term “change analysis” to emphasize the intelligence value obtained from large numbers of change detection over long time intervals, rather than the emphasis by most researchers to date on “change detection” methods and small numbers of change detections. Our methods emphasize local temporal descriptions of activities and include minimal spatial information about activities. Our three methods adapt and extend: (1) classic unsupervised pattern recognition operating on bag-of-words features; (2) Latent Semantic Analysis (LSA); and (3) probabilistic LSA (PLSA). These methods allow us to: (a) Detect and describe anomalous activities; (b) Discover categories of activity, describe a category of activity, and assign an activity to a category; (c) Retrieve similar activities from a historical database. We present experimental results that compare our methods (1)-(3) for performin...

• Type: Conference
• Year: 2008
• Where: AIPR
• Authors: Ray Rimey, Dan Keefe
• Keywords: AIPR 2008, Change Analysis, Change Detections, Image-derived Change Detections, Pattern Recognition