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Visual analysis of image collections

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Abstract

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-ImageProjection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

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References

  1. Jacob Goldberger, S.G., Greenspan, H.: Unsupervised image-set clustering using an information theoretic framework. IEEE Trans. Image Proc. 15(2), 449–458 (2006). doi:10.1109/TIP.2005.860593

    Article  Google Scholar 

  2. Kim, K., Jung, K., Park, S., Kim, H.: Support vector machines for texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1542–1550 (2002). doi:10.1109/TPAMI.2002.1046177

    Article  Google Scholar 

  3. Venkatesh, Y., Raja, S.: On the classification of multispectral satellite images using the Multi-Layer Perceptron. Pattern Recognit. 36(9), 2161–2175 (2003). doi:10.1016/S0031-3203(03)00013-X

    Article  MATH  Google Scholar 

  4. Paulovich, F.V., Oliveira, M.C.F., Minghim, R.: The projection explorer: A flexible tool for projection-based multidimensional visualization. In: Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), pp. 27–36. IEEE Computer Society, Washington DC (2007). doi:10.1109/SIBGRAPI.2007.39

    Chapter  Google Scholar 

  5. Paulovich, F.V., Nonato, L.G., Minghim, R., Levkowitz, H.: Least square projection: a fast high-precision multidimensional projection technique and its application to document mapping. IEEE Trans. Vis. Comput. Graph. 14(3), 564–575 (2008). doi:10.1109/TVCG.2007.70443

    Article  Google Scholar 

  6. Cuadros, A.M., Paulovich, F.V., Minhgim, R., Telles, G.P.: Point placement by phylogenetic trees and its application for visual analysis of document collections. In: IEEE Symposium Visual Analytics Science and Technology 2007 (VAST 2007), pp. 99–106. Sacramento, California, USA (2007). doi:10.1109/vast.2007.4389002

  7. Heijs, A.: Requirements for coordinated multiple view visualization systems for industrial applications. In: Proceedings V International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007), pp. 76–79. IEEE Computer Society, Washington DC (2007). doi:10.1109/CMV.2007.19

    Chapter  Google Scholar 

  8. Chen, C., Gagaudakis, G., Rosin, P.: Similarity-based image browsing. In: XVI IFIP World Computer Congress, International Conference on Intelligent Information Processing, pp. 206–213. Beijing, China (2000)

  9. Schvaneveldt, R.W. (ed.): Pathfinder Associative Networks: Studies in Knowledge Organization. Ablex, Norwood (1990)

    Google Scholar 

  10. Fan, J., Gao, Y., Luo, H.: Hierarchical classification for automatic image annotation. In: Proceedings XXX ACM International Conference on Research and Development in Information Retrieval, pp. 111–118. ACM Press, New York (2007). doi:10.1145/1277741.1277763

    Google Scholar 

  11. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000). doi:10.1126/science.290.5500.2323

    Article  Google Scholar 

  12. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000). doi:10.1126/science.290.5500.2319

    Article  Google Scholar 

  13. Moghaddam, B., Tian, Q., Lesh, N., Shen, C., Huang, T.S.: Visualization and user-modeling for browsing personal photo-libraries. Int. J. Comput. Vis. 56(1–2), 109–130 (2004)

    Article  Google Scholar 

  14. Yang, L.: Distance-preserving projection of high-dimensional data for nonlinear dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1243–1246 (2004). doi:10.1109/TPAMI.2004.66

    Article  Google Scholar 

  15. Minghim, R., Paulovich, F.V., Lopes, A.A.: Content-based text mapping using multi-dimensional projections for exploration of document collections. In: Erbacher, R.F., Roberts, J.C., Gröhn, M.T., Borner, K. (eds.) Proceedings SPIE-IS&T Electronic Imaging, Visualization and Data Analysis 2006, vol. 6060, p. 60600S. SPIE, San Jose (2006). doi:10.1117/12.650880

    Google Scholar 

  16. Jain, A.K., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997). doi:10.1109/34.574797

    Article  Google Scholar 

  17. Garson, G.D.: Interpreting neural net connection weights. AI Expert 6(4), 46–51 (1991)

    Google Scholar 

  18. Santos, D.P., Neto, J.E.S.B.: Feature selection with equalized salience measures and its application to segmentation. In: Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), pp. 253–262. IEEE Computer Society, Los Alamitos (2007). doi:10.1109/SIBGRAPI.2007.18

    Chapter  Google Scholar 

  19. Castellano, G., Fanelli, A.M.: Variable selection using neural-network models. Neurocomputing 31(1–4), 1–13 (2000). doi:10.1016/S0925-2312(99)00146-0

    Article  Google Scholar 

  20. Nath, R., Rajagopalan, B., Ryker, R.: Determining the saliency of input variables in neural network classifiers. Comput. Oper. Res. 24(8), 767–773 (1997). doi:10.1016/S0305-0548(96)00088-3

    Article  MATH  Google Scholar 

  21. Eler, D.M., Paulovich, F.V., de Oliveira, M.C.F., Minghim, R.: Coordinated and multiple views for visualizing text collections. In: Proceedings XXII International Conference on Information Visualization (IV’08), pp. 246–251 (2008). doi:10.1109/IV.2008.39

  22. Huang, K., Aviyente, S.: Rotation-invariant texture classification with ridgelet transform and Fourier transform. In: Proceedings of the ICIP, pp. 2141–2144. IEEE, New York (2006). doi:10.1109/ICIP.2006.312867

    Google Scholar 

  23. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996). doi:10.1109/34.531803

    Article  Google Scholar 

  24. Tuceryan, M., Jain, A.K.: Texture analysis. In: The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific, Singapore (1998)

    Google Scholar 

  25. da Silva, L.A., Moreno, R.A., Furuie, S.S., Hernandez, E.D.M.: Medical image categorization based on wavelet transform and self-organizing map. In: Proceedings VII International Conference on Intelligent Systems Design and Applications (ISDA 2007), pp. 353–356. IEEE Computer Society, Washington DC (2007). doi:10.1109/ISDA.2007.100

    Chapter  Google Scholar 

  26. Telles, G.P., Minghim, R., Paulovich, F.V.: Normalized compression distance for visual analysis of document collections. Comput. Graph. 31(3), 327–337 (2007). doi:10.1016/j.cag.2007.01.024

    Article  Google Scholar 

  27. Philips, D.C.: The development of crystallographic enzymology. ASF 30, 11–28 (1970)

    Google Scholar 

  28. Pietal, M.J., Tuszynska, I., Bujnicki, J.M.: Protmap2d: visualization, comparison and analysis of 2d maps of protein structure. Bioinformatics 23(11), 1429–1430 (2007). doi:10.1093/bioinformatics/btm124

    Article  Google Scholar 

  29. Altschul, S.F., Gish, W.M., Myers, E.W., Lipman, D.J.: A basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)

    Google Scholar 

  30. Cilibrasi, R., Vitányi, P.: Clustering by compression. IEEE Trans. Inf. Theory 51(4), 1546–1555 (2005). doi:10.1109/TIT.2005.844059

    Article  Google Scholar 

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Correspondence to Rosane Minghim.

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figuresAndAnnotations -371_2009_368_MOESM1_ESM.wmv (6.16MB)

imageDataSetExporation-371_2009_368_MOESM2_ESM.wmv (10.2MB)

medicalAnalysis-371_2009_368_MOESM3_ESM.wmv (4.56MB)

MLPclassificationAndFeaturesSelection-371_2009_368_MOESM4_ESM.wmv (7.31MB)

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Eler, D.M., Nakazaki, M.Y., Paulovich, F.V. et al. Visual analysis of image collections. Vis Comput 25, 923–937 (2009). https://doi.org/10.1007/s00371-009-0368-7

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