Abstract
The exponentially increasing amount of Earth Observation (EO) data requires novel approaches for data mining and exploration. Visual analytic systems have made valuable contribution in understanding the structure of data by providing humans with visual perception of data. However, these systems have limitations in dealing with large-scale high-dimensional data. For instance, the limitation in dimension of the display screen prevents visualizing high-dimensional data points. In this paper, we propose a virtual reality based visual analytic system, so called Immersive Information Mining, to enable knowledge discovery from the EO archive. In this system, Dimension Reduction (DR) techniques are applied to high-dimensional data to map into a lower-dimensional space to be visualized in an immersive 3D virtual environment. In such a system, users are able to navigate within the data volume to get visual perception. Moreover, they can manipulate the data and provide feedback for other processing steps to improve the performance of data mining system.
Keywords
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Bahmanyar, R., Datcu, M.: Measuring the semantic gap based on a communication channel model (2013)
van de Sande, K.E., Gevers, T., Snoek, C.G.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)
Choo, J., Lee, H., Liu, Z., Stasko, J., Park, H.: An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data. In: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, p. 865402 (2013)
Wise, J.A.: The ecological approach to text visualization. Journal of the American Society for Information Science 50(13), 1224–1233 (1999)
Stasko, J., Görg, C., Liu, Z.: Jigsaw: supporting investigative analysis through interactive visualization. Information Visualization 7(2), 118–132 (2008)
Jeong, D.H., Ziemkiewicz, C., Fisher, B., Ribarsky, W., Chang, R.: ipca: An interactive system for pca-based visual analytics, vol. 28, pp. 767–774. Wiley Online Library (2009)
Azzag, H., Picarougne, F., Guinot, C., Venturini, G., et al.: Vrminer: A tool for multimedia database mining with virtual reality. In: Processing and Managing Complex Data for Decision Support, pp. 318–339 (2005)
Nakazato, M., Huang, T.S.: 3d mars: Immersive virtual reality for content-based image retrieval. In: IEEE International Conference on Multimedia and Expo, vol. 46 (2001)
Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data-challenges in human-computer interaction & biomedical informatics. In: 9th International Joint Conference on e-Business and Telecommunications (ICETE 2012), pp. IS9–IS20 (2012)
Wong, B.L.W., Xu, K., Holzinger, A.: Interactive visualization for information analysis in medical diagnosis. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 109–120. Springer, Heidelberg (2011)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Hinton, G., Roweis, S.: Stochastic neighbor embedding. Advances in Neural Information Processing Systems 15, 833–840 (2002)
Chen, J., Shan, S., Zhao, G., Chen, X., Gao, W., Pietikainen, M.: A robust descriptor based on weber’s law. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)
Jolliffe, I.T.: Principal component analysis, vol. 487. Springer, New York (1986)
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.: Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, pp. 41–48. IEEE (1999)
Seung, D., Lee, L.: Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Chen, L., Buja, A.: Local multidimensional scaling for nonlinear dimension reduction, graph layout and proximity analysis. PhD thesis, Citeseer (2006)
Chen, L., Buja, A.: Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. Journal of the American Statistical Association 104(485), 209–219 (2009)
Venna, J., Kaski, S.: Local multidimensional scaling. Neural Networks 19(6), 889–899 (2006)
Lee, J.A., Verleysen, M.: Nonlinear dimensionality reduction. Springer (2007)
Lee, J.A., Verleysen, M.: Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing 72(7), 1431–1443 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Babaee, M., Rigoll, G., Datcu, M. (2013). Immersive Interactive Information Mining with Application to Earth Observation Data Retrieval. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds) Availability, Reliability, and Security in Information Systems and HCI. CD-ARES 2013. Lecture Notes in Computer Science, vol 8127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40511-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-40511-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40510-5
Online ISBN: 978-3-642-40511-2
eBook Packages: Computer ScienceComputer Science (R0)