Application of statistical physics for the identification of important events in visual lifelogs

Dementia is one of the most common diseases in the elderly people. Experience shows that Microsoft´s SenseCam can be an effective memory-aid device, as it helps users to improve recollecting an experience by creating visual lifelogs. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge to deconstruct a sizeable collection of images into meaningful events for users. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to “distinct significant events” in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images.