CD-ARES 2013: Availability, Reliability, and Security in Information Systems and HCI pp 376-386 | Cite as
Immersive Interactive Information Mining with Application to Earth Observation Data Retrieval
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
Immersive visualization Information mining Dimension reductionReferences
- 1.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)Google Scholar
- 2.Bahmanyar, R., Datcu, M.: Measuring the semantic gap based on a communication channel model (2013)Google Scholar
- 3.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)CrossRefGoogle Scholar
- 4.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)Google Scholar
- 5.Wise, J.A.: The ecological approach to text visualization. Journal of the American Society for Information Science 50(13), 1224–1233 (1999)CrossRefGoogle Scholar
- 6.Stasko, J., Görg, C., Liu, Z.: Jigsaw: supporting investigative analysis through interactive visualization. Information Visualization 7(2), 118–132 (2008)CrossRefGoogle Scholar
- 7.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)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 10.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)Google Scholar
- 11.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)CrossRefGoogle Scholar
- 12.Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
- 13.Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)MATHCrossRefGoogle Scholar
- 14.Hinton, G., Roweis, S.: Stochastic neighbor embedding. Advances in Neural Information Processing Systems 15, 833–840 (2002)Google Scholar
- 15.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)Google Scholar
- 16.Jolliffe, I.T.: Principal component analysis, vol. 487. Springer, New York (1986)CrossRefGoogle Scholar
- 17.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)Google Scholar
- 18.Seung, D., Lee, L.: Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)Google Scholar
- 19.Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
- 20.
- 21.
- 22.Chen, L., Buja, A.: Local multidimensional scaling for nonlinear dimension reduction, graph layout and proximity analysis. PhD thesis, Citeseer (2006)Google Scholar
- 23.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)MathSciNetCrossRefGoogle Scholar
- 24.Venna, J., Kaski, S.: Local multidimensional scaling. Neural Networks 19(6), 889–899 (2006)MATHCrossRefGoogle Scholar
- 25.Lee, J.A., Verleysen, M.: Nonlinear dimensionality reduction. Springer (2007)Google Scholar
- 26.Lee, J.A., Verleysen, M.: Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing 72(7), 1431–1443 (2009)CrossRefGoogle Scholar