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Texture Classification for Multivariate Data Visualization Using Spectral Decomposition

This presentation by Cheong Hee Park, guided by advisor Victoria Interrante, explores innovative methods for visualizing multivariate datasets in a 2D planar format through the analysis of texture perceptual features. The approach consists of two main steps: first, classifying textures into meaningful categories based on directionality, regularity, and structural grouping; second, synthesizing textures to effectively convey multivariate data values. The presentation reviews various texture analysis methods, such as the Discrete Fourier Transform, while discussing future work on visualizing magnetic fields and improving texture-based data representation.

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Texture Classification for Multivariate Data Visualization Using Spectral Decomposition

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  1. Texture Classification using Spectral Decomposition Presenter: Cheong Hee Park Advisor: Victoria Interrante

  2. Overview Goal: Visualization of multivariate data set in a planar 2D using principal perceptual features of texture. • Step1: Classify textures into meaningful categories. • Classification by directionality • Classification by regularity • Structural grouping • Step2: Synthesize a series of textures to convey values of multivariate data.

  3. Review of texture analysis and data visualization • Discrete Fourier Transform • Classification by directionality • Classification by regularity • Classification by Structure • Future work

  4. Visualization of Magnetic fieldusing orientation, size and contrast Using Visual Texture for Information Display - Colin Ware and William Knight (1995)

  5. Display over a 3D surface using height, density and regularity Building Perceptual Textures to Visualize Multidimensional Datasets (C. Healey, J. Enns, 1998 )

  6. Harnessing natural textures for multivariate visualization (Victoria Interrante) farms(percent) in 1992 percent change of farms from 1987 to 1992

  7. What is texture? • An image composed of uniform or non-uniform repetition of natural or artificial patterns • Methods used for texture analysis • Autocorrelation • Co-occurrence based method • Parametric models of texture • Gray level run length • Spectral decomposition

  8. Principal features of texture • Directionality:directional vs non-directional • Coarseness: coarse vs fine • Contrast: high contrast vs low contrast • Regularity:regular vs irregular (periodicity, randomness) • Line likeness: line-like vs blob-like • Roughness: rough vs smooth

  9. Toward a texture naming system: identifying relevant dimensions of texture(A.R.Rao, G.L.Lohse, 1996) Marble-like Lace-like Directional, Locally-oriented Random, Non granular, Somewhat repetitive Non-random, Repetitive, non-directional <-> directional random Random, granular

  10. Texture features corresponding to visual perception -Tamura, Mori and Yamawaki psychological measurement of directionality (by human subjects using pair comparison method) computational measurement of directionality (using local vertical and horizontal directional operators)

  11. Modeling spatial and temporal textures - Fang Liu • Decomposition of texture into three components based on Wold theory: harmonic(periodicity), evanescent(directionality), indeterministic(random). • Measured deterministic energy from harmonic and evanescent components, and indeterministic energy from indeterministic component.

  12. DFT deterministic indeterministic • Used energy measurements for texture modeling and image retrieval

  13. Discrete Fourier Transform Given an image y(m,n), DFT IDFT

  14. Y(l,k) in a frequency domain represents the response of cosine and sine filters.

  15. Freq uency Hanning window DFT filtering

  16. regularity directionality

  17. Directionality 10 f 0 --------- 17 f 0 Directionality = (K; number of columns)

  18. 27 textures with highest directionality

  19. The 27 middle directional textures

  20. 27 textures with lowest directionality

  21. directionality

  22. Instead of two processes FFT and local window interpolation, apply global sinusoidal filters directly to the texture

  23. Directionality from direct filtering

  24. - Psychological experiment by Tamura • Ours(by interpolation) • (by direct filtering) • - computational experiment by Tamura Q: How can we judge which method is better ?

  25. Pattern regularity as a visual keyD. Chetverikov using autocorrelation of gray intensities

  26. Regularity (A: overlapping area) dominant direction }i Regularity = max f – min f i height/2

  27. Regularityclassification

  28. Directionality Regularity

  29. Directionality Regularity (by direct filtering)

  30. Structural grouping Absolute Difference (L1 norm)

  31. brick-like net-like

  32. granular line-like

  33. Future workHow to map attributes of multivariate data to texture perceptual dimensions independently? • What perceptual features of texture are most orthogonal? -- Minimize interference when they are combined for display of multivariate data. • Mapping should be continuous within an attribute and make maximum distinction between attributes.

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