Abstract

Even though Microsoft’s SeneseCam can be effective as a memory-aid device, there exists a substantial challenge in effectively managing the vast amount of images that are maintained by this device. Deconstructing a sizeable collection of images into meaningful events for users represents a significant task. Such events may be identified by applying Random Matrix Theory (RMT) to a cross-correlation matrix C that has been constructed using SenseCam lifelog data streams. Overall, the RMT technique proves promising for major event detection in SenseCam images.