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Random matrix ensembles of time correlation matrices to analyze visual lifelogs

Li, Na, Crane, Martin orcid logoORCID: 0000-0001-7598-3126, Ruskin, Heather J. orcid logoORCID: 0000-0001-7101-2242 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2013) Random matrix ensembles of time correlation matrices to analyze visual lifelogs. MultiMedia Modeling (Lecture Notes in Computer Science), 8325 . pp. 400-411. ISSN 0302-9743

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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Random Matrix Theory; Correlation & Covariance
Subjects:Computer Science > Lifelog
Mathematics > Mathematical models
Mathematics > Statistics
Physical Sciences > Statistical physics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Springer Verlag
Official URL:http://link.springer.com/chapter/10.1007%2F978-3-3...
Copyright Information:© 2013 Springer Verlag The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:19672
Deposited On:15 May 2014 10:54 by Martin Crane . Last Modified 04 Feb 2020 14:51
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