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Random Matrix Ensembles of Time Correlation Matrices to Analyze Visual Lifelogs

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MultiMedia Modeling (MMM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8325))

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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.

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References

  1. Hodges, S., Williams, L., Berry, E., Izadi, S., Srinivasan, J., Butler, A., Smyth, G., Kapur, N., Wood, K.R.: Sensecam: A retrospective memory aid. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 177–193. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  2. Goldman, D.: Google unveils project glass virtual-reality glasses. Money (CNN) (2012)

    Google Scholar 

  3. Bell, G., Gemmell, J.: A digital life. Scientific American (2007)

    Google Scholar 

  4. Doherty, A.R., Smeaton, A.F.: Automatically segmenting lifelog data into events. In: WIAMIS, pp. 20–23 (2008)

    Google Scholar 

  5. Li, N., Crane, M., Ruskin, H.J., Gurrin, C.: Multiscaled cross-correlation dynamics on sensecam lifelogged images. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part I. LNCS, vol. 7732, pp. 490–501. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  6. Wigner, E.P.: On the statistical distribution of the widths and spacings of nuclear resonance levels. Mathematical Proc. of the Cambridge Philosophical Society 47(10), 790–798 (1951)

    Article  MATH  Google Scholar 

  7. Dyson, F.J.: Statistical theory of the energy levels of complex systems i. Journal of Mathematical Physics 3, 140–157 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  8. Dyson, F.J., Mehta, M.L.: Statistical theory of the energy levels of complex systems iv. Journal of Mathematical Physics 4, 701–712 (1963)

    Article  MATH  MathSciNet  Google Scholar 

  9. Mehta, M.L., Dyson, F.J.: Statistical theory of the energy levels of complex systems v. Journal of Mathematical Physics 4, 713–719 (1963)

    Article  MATH  MathSciNet  Google Scholar 

  10. Plerou, V., Gopikrishnan, P., Rosenow, B., Nunes Amaral, L.A., Guhr, T., Stanley, H.E.: Random matrix approach to cross correlations in financial data. Physical Review E 65(6), 066126+ (2002)

    Google Scholar 

  11. Daskala, B., Askoxylakis, I., Brown, I., Dickman, P., Friedewald, M., Irion, K., Kosta, E., Langheinrich, M., McCarthy, P., Osimo, D., Papiotis, S., Pasic, A., Petkovic, M., Price, B., Spiekermann, S., Wright, D.: Risks and benefits of emerging life-logging applications. In: Final report, European Network and Information Security Agency (ENISA) (November 2011)

    Google Scholar 

  12. Mann, S.: Wearable computing: a first step toward personal imaging. Computer 30 (1997)

    Google Scholar 

  13. Gemmell, J., Bell, G., Lueder, R.: Mylifebits: a personal database for everything. Commun. ACM 49(1), 88–95 (2006)

    Article  Google Scholar 

  14. Ashbrook, D., Lyons, K., Clawson, J.: Capturing experiences anytime, anywhere. IEEE Pervasive Computing 5(2), 8–11 (2006)

    Article  Google Scholar 

  15. Lin, W.H., Hauptmann, A.: Structuring continuous video recordings of everyday life using time-constrained clustering. In: Multimedia Content Analysis, Management, and Retieval SPIE-IST Electronic Imaging, vol. 6073, pp. 111–119 (2006)

    Google Scholar 

  16. Lee, M.L., Dey, A.K.: Providing good memory cues for people with episodic memory impairment. In: ASSETS, pp. 131–138 (2007)

    Google Scholar 

  17. Conrey, J.B., Farmer, D.W., Keating, J.P., Rubinstein, M.O., Snaith, N.C.: Integral moments of l-functions. Proceedings of the London Mathematical Society 91, 33–104 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  18. Tulino, A.M., Verdú, S.: Random matrix theory and wireless communications. Foundations and Trends in Communications and Information Theory 1(1) (2004)

    Google Scholar 

  19. Chirikjian, G.S.: Multivariate statistical analysis and random matrix theory. Applied and Numerical Harmonic Analysis 2, 229–270 (2012)

    MathSciNet  Google Scholar 

  20. Ulfarsson, M.O., Solo, V.: Dimension estimation in noisy pca with sure and random matrix theory. IEEE Transactions on Signal Processing 56(12), 5804–5816 (2008)

    Article  MathSciNet  Google Scholar 

  21. Conlon, T., Ruskin, H.J., Crane, M.: Random matrix theory and fund of funds portfolio optimisation. Physica A: Statistical Mechanics and its Applications (2), 565–576 (2010)

    Google Scholar 

  22. Berry, E., Kapur, N., Williams, L., Hodges, S., Watson, P., Smyth, G., Srinivasan, J., Smith, R., Wilson, B., Wood, K.: The use of a wearable camera, sensecam, as a pictorial diary to improve autobiographical memory in a patient with limbic encephalitis. Neuropsychological Rehabilitation 17(2), 580–601 (2007)

    Google Scholar 

  23. Conlon, T., Ruskin, H.J., Crane, M.: Multiscaled cross-correlation dynamics in financial time-series. Advances in Complex Systems (ACS) 12(04), 439–454 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  24. Conlon, T., Ruskin, H.J., Crane, M.: Cross-correlation dynamics in financial time series. Papers 1002.0321, arXiv.org (February 2010)

  25. Lee, P.A., Ramakrishnan, T.V.: Disordered electronic systems. Rev. Mod. Phys. 57, 287–337 (1985)

    Article  Google Scholar 

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Li, N., Crane, M., Ruskin, H.J., Gurrin, C. (2014). Random Matrix Ensembles of Time Correlation Matrices to Analyze Visual Lifelogs. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-04114-8_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04113-1

  • Online ISBN: 978-3-319-04114-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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