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Random Matrix Ensembles of Time Correlation Matrices to Analyze Visual Lifelogs

Authors: 

Na Li, Martin Crane, Heather J. Ruskin, Cathal Gurrin

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Visual lifelogging is the process of automatically recording images and other sensor data for the purpose of aiding memory recall. Such lifelogs are usually created using wearable cameras. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge for users to deconstruct a sizeable collection of images into meaningful events. 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.
Conference Name: 
MultiMedia Modeling (Lecture Notes in Computer Science)
Proceedings: 
20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part I
Digital Object Identifer (DOI): 
10.1007/978-3-319-04114-8_34
Publication Date: 
31/12/2013
Conference Location: 
Ireland
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes