Texture Classification
using Spectral Decomposition
Presenter: Cheong Hee Park
Advisor: Victoria Interrante
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.
 Review
of texture analysis and
data visualization
 Discrete Fourier Transform
 Classification by directionality
 Classification by regularity
 Classification by Structure
 Future 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 length
Spectral 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

Marble-like
Lacelike
Directional,
Locally-oriented
Non-random,
Repetitive,
non-directional <-> directional
Toward a texture naming system: identifying relevant
dimensions of texture(A.R.Rao, G.L.Lohse, 1996)
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.
DFT
deterministic
indeterministic
 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.
F
r
e
q
u
e
n
c
y
Hanning window
DFT
filtering
regularity
directionality
Directionality
10
f
f
0
Directionality =
0 --------- 17
(K; number of columns)
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
Directionality
from
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
dominant
direction
}i
(A: overlapping area)
i
height/2
Regularity
= max f – min f
Regularity
classification
Directionality
Regularity
Directionality
Regularity
(by direct filtering)
Structural grouping
Absolute
Difference
(L1 norm)
brick-like
net-like
line-like
granular
Future work
How 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.
