Presenter: Cheong Hee Park

Advisor: Victoria Interrante

Texture Classification using Spectral Decomposition

OverviewGoal: 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.

Review of texture analysis and data visualization

Discrete Fourier TransformClassification by directionalityClassification by regularityClassification by StructureFuture work

Visualization of Magnetic field using orientation, size and contrast

Using Visual Texture for Information Display - Colin Ware and William Knight (1995)

Display over a 3D surface using height, density and regularity

Building Perceptual Textures to Visualize Multidimensional Datasets (C. Healey, J. Enns, 1998 )

Harnessing natural textures for multivariate visualization (Victoria Interrante)

farms(percent) in 1992 percent change of farms from 1987 to 1992

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 lengthSpectral decomposition

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

Toward a texture naming system: identifying relevant dimensions of texture(A.R.Rao, G.L.Lohse, 1996)

Lace-like

Directional,Locally-oriented

Non-random,Repetitive,

non-directional <-> directional

Marble-like

Random,Non granular,

Somewhat repetitive

random

Random,granular

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)

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.

deterministic indeterministic

DFT

Used energy measurements for texture modeling and image retrieval

Discrete Fourier Transform Given an image y(m,n),

DFT

IDFT

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

Hanning window

DFT

filtering

Frequency

directionality

regularity

Directionality

0 --------- 17

0

10

Directionality =

(K; number of columns)

f

f

27 textures with highest directionality

The 27 middle directional textures

27 textures with lowest directionality

directionality

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

Directionalityfrom

direct filtering

- Psychological experiment by Tamura- Ours(by interpolation)- (by direct filtering)- computational experiment by Tamura

Q: How can we judge which method is better ?

Pattern regularity as a visual key D. Chetverikov

using autocorrelation of gray intensities

Regularity

(A: overlapping area)

dominant direction

height/2

i Regularity

= max f – min f

}i

Regularityclassification

Directionality

Regularity

Directionality

Regularity

(by direct filtering)

Structural grouping

Absolute Difference(L1 norm)

brick-like net-like

granular

line-like

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.