LARGE-SCALE-INVARIANT TEXTURE RECOGNITION

Muhammad Rushdi and Jeffrey Ho

Computer and Information Science and Engineering, University of Florida, Gainesville, U.S.A.

Keywords:

Texture classiﬁcation, Scale-invariance, Gray-level co-occurrence matrices.

Abstract:

This paper addresses the problem of texture recognition across large scale variations. Most of the exist-

ing methods for texture recognition handle only small-scale variations in test images. We propose using

microscopic-scale textures to classify texture images at any coarser scale without prior knowledge of the rel-

ative scale. In particular, given a test camera image, we compute the average error of approximating the test

texture with patches of the microscopic texture for certain category and scaling factor. Recognition is made

by selecting the category with the minimum average error over all categories and scaling factors. Experiments

on camera and low-magniﬁcation microscopic images show the validity of the proposed method.

1 INTRODUCTION

This paper explores the problem of classifying tex-

ture across large scale variations. In particular, using

high-magniﬁcation microscopic textures, we aim to

classify textures at any coarser scale. The difﬁculty

of the problem stems from several facts. First, im-

ages of the same material with large variations of the

imaging scale may appear so different even for a hu-

man observer (Figure 1). Second, accurate and fast

techniques need to be developed to relate the mate-

rial appearances at different scales. Although a lot

of work has been done in the area of texture recogni-

tion (Davies, 2008), (Varma and Zisserman, 2009),

little attention has been made to the effect of large

scale variations. The CUReT database (Dana and

Koenderink, 1999) captures texture images for 61 cat-

egories where each category is represented by 205 im-

ages of different viewing and illumination conditions.

However, this database lacks examples of scale vari-

ation except for 4 materials that have slightly scaled

images. Varma and Zisserman (Varma and Zisser-

man, 2009) claim that their MRF texture model is not

adversely affected by scale changes. However, their

experiments were done on the aforementioned scaled

CUReT images which have only a small scale factor

of 2. Kang (Kang and Nagahashi, 2005) developed

a framework for scale-invariant texture analysis using

multi-scale local autocorrelation features. Neverthe-

less, the experiments were limited to small changes in

scale ranging from 0.7 to 1.3. Leung and Peterson

(Leung and Peterson, 1992) used moment-invariant

and log-polar features to classify texture. However,

scale variations in their experiments were limited to

0.5, 0.67, and 1.0.

Our contribution in this paper is threefold. Firstly,

we introduce a new approach for classifying texture

across large scale variations. In particular, we show

how an approximation of a test image using micro-

scopic textures can be used to recognize textures at

any scale. Secondly, we employ our approach to es-

timate the relative scale of a test image with respect

to microscopic texture. Thirdly, we provide a dataset

of multi-scale textures that can be used to assess the

robustness of texture classiﬁers to scale changes.

2 COLLECTING MULTISCALE

TEXTURES

Many texture databases are freely available including

the CUReT database (Dana and Koenderink, 1999)

and the UIUC database (Lazebnik and Ponce, 2005).

While these databases sample reasonably the varia-

tions in illumination and viewing points, none of them

properly captures scale variations of the textured ma-

terials. To ﬁll this gap, we started collecting multi-

scale texture data. In this paper, we show experiments

on ﬁve categories of materials that have challenging

textural patterns: cloth, loofa, marble, sponge, and

granite plaster (Figure 1). For every category, we

captured images using two imaging devices. Firstly,

camera texture images were collected using a high-

resolution 8-MB digital camera. Twenty images were

taken at different distances, angles and illumination

442

Rushdi M. and Ho J..

LARGE-SCALE-INVARIANT TEXTURE RECOGNITION.

DOI: 10.5220/0003398904420445

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2011), pages 442-445

ISBN: 978-989-8425-47-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Sponge

Marble

Loofa

Cloth

Plaster

Digital

Camera

Low−Magnification

Microscope

High−Magnification

Microscope

Figure 1: Multi-scale texture samples. Columns represent

images taken by a digital camera, a microscope at low and

high magniﬁcation, respectively. Each row shows images

for one type of texture.

conditions. Secondly, microscopic texture images

were collected using a low-cost hand-held digital mi-

croscope. The microscope has a magniﬁcation power

of up to 230X. However, sharp microscopic images

can be realized at magniﬁcation powers of about 60X

and 220X. For each category, we collected at least 10

images at the low-magniﬁcation level and 20 images

at the high-magniﬁcation level.

3 ALGORITHM

Our goal is to build a system that uses microscopic

textures to correctly classify textures at any coarser

scale even without prior knowledge of the relative

scale between a test image and the microscopic tex-

tures. The steps of our algorithm are as follows. First,

we create downsized versions of the microscopic im-

ages that have sizes ranging from 10× 10 pixels up

to 200 × 200 pixels (the standard size of test images

in our experiments). Second, for each of these down-

sized versions, we calculate Haralick texture features

for each of the red, green, and blue color planes as

will be explained in Section 4. Then, we store the

calculated features of the high-magniﬁcation micro-

scopic images. Third, given a test texture image, we

subdivide the image into patches of sizes correspond-

ing to those used with the microscopic images. For

each patch size selection and each candidate category,

we compute the Haralick texture features for the test

patches and ﬁnd the average Euclidean distance from

the feature vector of each test patch to those of the

downsized microscopic images of the candidate cate-

gory. After that, we sum the average distance over all

the test patches. Finally, the output class for the given

test image is chosen to be the one that has the min-

imum total average distance. The minimum is taken

over all categories and all scaling factors since that

the relative scaling factor between the test image and

the microscopic images is generally unknown.

4 GRAY-LEVEL

CO-OCCURRENCE MATRICES

Interactions of neighbour pixels in texture can be de-

scribed by second-order features that are derivedfrom

the Gray-Level Co-occurrence Matrices (GLCM)

(Haralick, 1979). These matrices are deﬁned as fol-

lows. Given a position operator P(i, j), let A be an

n× n matrix whose element A(i, j) is the number of

times that points with gray level (intensity) g

occur,

in the position speciﬁed by P, relative to points with

gray level g

. Let C be the n × n matrix that is pro-

duced by dividing A with the total number of point

pairs that satisfy P. The element C(i, j) is a measure

of the joint probability that a pair of points satisfying

P will have the values g

, g

, respectively. C is called a

co-occurrence matrix deﬁned by P. Features derived

from such matrices exhibit high distinctive power and

relative invariance under large scale variations. We

use three of these features:

Contrast =

G−1

∑

i=0

G−1

∑

j=0

|i− j|

C(i, j) (1)

Energy =

G−1

∑

i=0

G−1

∑

j=0

C(i, j)

(2)

Homogeneity =

G−1

∑

i=0

G−1

∑

j=0

C(i, j)

1+ |i− j|

(3)

5 EXPERIMENTS AND RESULTS

5.1 Classifying Camera Images with

Fixed Scales

First, we ran the algorithm with a ﬁxed scale. Specif-

ically, all of the high-magniﬁcation microscopic im-

ages were downsized to one ﬁxed size and compared

LARGE-SCALE-INVARIANT TEXTURE RECOGNITION

443

with patches from the test camera image of the same

size. The recognition results for ﬁxed sizes ranging

from 20× 20 to 50× 50 pixels are shown in Figure 2.

The best recognition rate of 82 % occurred at a patch

size of 40 × 40. However, the per-category perfor-

mance curves show that each material is better recog-

nized at a different scale. This suggests that we vary

the scale and let the test images choose their right rel-

ative scale.

5.2 Classifying Camera Images

with Variable Scales

As we suggested in Subsection 5.1, we ran the algo-

rithm for a range of patch sizes 10, 20, 30, 40, and 50

concurrently. The results are shown in Table 1. The

recognition rate is 82%. Despite this rate is no im-

provement over the one achieved with a ﬁxed patch

size of 40 × 40, we still have an important advan-

tage here. In most cases, we don’t know the relative

scale difference between the test camera image and

the microscopic textures. Using our algorithm with

a variable scale will return an estimate of this scale

along with the category of the test image. For exam-

ple, of all of the 18 images of the loofa texture that

were correctly classiﬁed: 7 images were assigned a

scale of 4:1 (patch size 50× 50), 8 images were as-

signed a scale of 5:1 (patch size 40×40), and 3 image

were assigned a scale of 6.67:1 (patch size 30 × 30).

These estimated scales closely match the actual image

scales.

5.3 Classifying Low-magniﬁcation

Microscopic Textures with Fixed

Scales

To examine the ability of our algorithm to deal with

different scales, we tested the algorithm with mi-

croscopic images taken at the lower magniﬁcation

power of the microscope. Again, we ran the algo-

rithm with ﬁxed scales. That is, all of the high-

magniﬁcation microscopic images are downsized to

one ﬁxed size and compared with patches from the

test low-magniﬁcation microscopic image of the same

size. The recognition results for ﬁxed sizes ranging

from 20× 20 to 200 × 200 pixels are shown in Fig-

ure 3. The best rate of 98% occurs at a patch size

ranging from 60× 60 to 90 × 90. This result is inter-

esting in two aspects. First, it makes sense to see this

improvement over the case of camera images since

the low-magniﬁcation microscopic images are closer

in scale and appearance to the high-magniﬁcation mi-

croscopic images. Second, if we compare Figures 2

20 40 60 80 100 120 140 160 180 200

100

Patch size

Classification rate

Cloth

Loofa

Marble

Sponge

Plaster

Total Rate

Figure 2: Recognition rate of test camera images as patch

size varies. The best overall rate of 82% occurs at a patch

size of 40× 40.

20 40 60 80 100 120 140 160 180 200

100

Patch size

Classification rate

Cloth

Loofa

Marble

Sponge

Plaster

Total Rate

Figure 3: Recognition rate of low-magniﬁcation micro-

scopic images as patch size varies. The best overall rate

of 98 % occurs for a range of patch sizes between 60× 60

and 90× 90.

and 3, we will notice an upward shift of the patch size

where optimal recognition rate occurs. This indeed

agrees with the intuition. Since the low-magniﬁcation

and high microscopic images are closer in scaling,

then the right scale should be smaller (i.e., the patch

size should be larger).

5.4 Classifying Low-magniﬁcation

Microscopic Textures with Variable

Scales

We retested our algorithm on the low-magniﬁcation

microscopic images after allowing the patch size to

vary from 60 × 60 to 90× 90 (the optimal patch size

range returned by the ﬁxed-scale experiments). The

results are shown in Table 2. The overall rate is 98%

which is the same as that of the ﬁxed-scaling exper-

iment. Again, we still get the bonus that the right

scale is returned for each test image. For example, all

VISAPP 2011 - International Conference on Computer Vision Theory and Applications

444

Table 1: Confusion matrix for classifying camera images of

Cloth (C), Loofa (L), Marble (M), Sponge (S), and Granite

Plaster (P) using high-magniﬁcation microscopic images.

The overall rate is 82%.

Material C L M S P

C 90 10 0 0 0

L 0 90 0 40 0

M 0 0 100 5 25

S 0 0 0 55 0

P 10 0 0 0 75

Table 2: Confusion matrix for classifying low-

magniﬁcation microscopic images using high-

magniﬁcation ones. The overall rate is 98%.

Material C L M S P

C 90 0 0 0 0

L 10 100 0 0 0

M 0 0 100 0 0

S 0 0 0 100 0

P 0 0 0 0 100

of the 10 images of the loofa texture were correctly

classiﬁed and additionally their estimated scales were

returned: 7 images were assigned a scale of 2.86:1

(patch size 70 × 70), 2 images were assigned a scale

of 2.5:1 (patch size 80 × 80), and 1 image was as-

signed a scale of 3.33:1 (patch size 60 × 60). These

estimated scales match roughly the actual ones.

6 CONCLUSIONS

AND FUTURE WORK

We showed in this paper how microscopic images

can be used to classify texture at coarser scales. In

the future, we plan to expand our multi-scale texture

dataset to complement the existing texture databases

and learn more about the relationship between tex-

tures at different scales.

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