Material Classiﬁcation in the Wild: Do Synthesized Training Data

Generalise Better than Real-world Training Data?

Grigorios Kalliatakis

, Anca Sticlaru

, George Stamatiadis

, Shoaib Ehsan

, Ales Leonardis

Juergen Gall

and Klaus D. McDonald-Maier

School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.

School of Computer Science, University of Birmingham, Birmingham, U.K.

Institute of Computer Science, University of Bonn, Bonn, Germany

Keywords:

Material Classiﬁcation, Synthesized Data, CNN.

Abstract:

We question the dominant role of real-world training images in the ﬁeld of material classiﬁcation by investi-

gating whether synthesized data can generalise more effectively than real-world data. Experimental results on

three challenging real-world material databases show that the best performing pre-trained convolutional neural

network (CNN) architectures can achieve up to 91.03% mean average precision when classifying materials in

cross-dataset scenarios. We demonstrate that synthesized data achieve an improvement on mean average pre-

cision when used as training data and in conjunction with pre-trained CNN architectures, which spans from ∼

5% to ∼ 19% across three widely used material databases of real-world images.

1 INTRODUCTION

Material classiﬁcation in real-world environments is a

challenging problem due to the huge impact of view-

ing and illumination conditions on material appear-

ance. Therefore, training an appropriate classiﬁer re-

quires a training set which covers all these conditions

as well as the intra-class variance of the materials.

Two approaches are mainly used to generate suitable

training sets: 1) capture a single representative per

material category under a multitude of different con-

ditions, such as scale, illumination and viewpoint, in

a controlled setting (Caputo et al., 2005; Dana et al.,

1999; Hayman et al., 2004; Liu et al., 2013) 2) use

images acquired under uncontrolled conditions (e.g.,

FMD (Sharan et al., 2010) which is generated by tak-

ing images from an internet image database (Flickr)).

For (1), the measured viewing and illumination

conﬁgurations are rather coarse and hence not de-

scriptive enough to capture the mesoscopic effects in

material appearance, which consider the light inter-

action with material surface regions mapped to ap-

proximately one pixel, in an accurate way. Moreover,

the material samples are only measured under con-

trolled illumination or lab environments which may

not generalise to material appearance under complex

realworld scenarios. On the other hand, approach (2)

has the advantage that both the intra-class variance

of materials and the environment conditions are sam-

pled in a representative way. Unfortunately, the im-

ages have to be collected manually, and the materials

appearing in the image have to be segmented and an-

notated. The necessary effort again severely limits the

number of conﬁgurations that can be generated this

way.

Motivated by the success of synthesized data for

different vision applications (e.g. (Vazquez et al.,

2014; Enzweiler and Gavrila, 2008; Pishchulin et al.,

2011; Targhi et al., 2008; Stark et al., 2010; Shot-

ton et al., 2011; Oxholm and Nishino, 2012; Bar-

ron and Malik, 2012; Barron and Malik, 2013)), we

question the dominant role of real-world images in

the ﬁeld of material classiﬁcation and investigate me-

thodically whether synthetic datasets generalise bet-

ter than real-world datasets when applied as train-

ing datasets and in conjunction with CNN architec-

tures. For performing the large set of experiments,

we partly followed the approach of Chatﬁeld et al.

(Chatﬁeld et al., 2014) which was used for compar-

ing CNN architectures for recognition of object cat-

egories. We, on the other hand, tackle material clas-

siﬁcation in this particular work, an entirely differ-

ent problem from (Chatﬁeld et al., 2014). Continu-

ing from our previous work (Kalliatakis et al., 2017),

we go one step further by investigating the effect of

training datasets (both real-world and synthetic) on

Kalliatakis, G., Sticlaru, A., Stamatiadis, G., Ehsan, S., Leonardis, A., Gall, J. and McDonald-Maier, K.

Material Classiﬁcation in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?.

DOI: 10.5220/0006634804270432

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

427-432

ISBN: 978-989-758-290-5

427

Table 1: Cross-dataset material classiﬁcation results. Training and testing are performed using 3 different databases of real-

world images. The name on the left denotes the training database, while the name on the right implies the testing database.

Bold font highlights the leading mean result for every experiment. Three data augmentation strategies are used for both

training and testing: 1) no augmentation (denoted Image Aug=-), 2) ﬂip augmentation (denoted Image Aug=(F)), 3) crop and

ﬂip (denoted Image Aug=(C)). Augmented images are used as stand-alone samples (f), or by combining the corresponding

descriptors using sum (s) or max (m) pooling or stacking (t). Here, GS denotes gray scale. The same symbols for data

augmentation options and gray scale are used in the rest of the paper. For instance, if we take the ﬁrst row, it means that the

C+F augmentation is used to generate the new images during the training phase and is further combined with the sum pooling

in the testing phase (s).

Image Aug. FMD - ImageNet FMD - MINC-2500 MINC-2500 - ImageNet7

Method

Training Testing mAP mAP mAP

(m) CNN F (C) f s 78.23% 71.87% 85.11%

(n) CNN S (C) f s 83.49% 72.95% 86.18%

(o) CNN M 82.40% 73.06% 87.64%

(p) CNN M (C) f s 81.68% 74.82% 85.79%

(q) CNN M (C) f m 81.69% 75.46% 86.55%

(r) CNN M (C) s s 79.52% 73.56% 89.88%

(s) CNN M (C) t t 80.22% 74.19% 89.53%

(t) CNN M (C) f 80.31% 73.83% 82.71%

(u) CNN M (F) f 81.91% 73.01% 91.03%

(v) CNN M GS f 71.82% 66.78% 89.37%

(w) CNN M GS (C) s 75.95% 69.05% 87.87%

(x) CNN M 2048 (C) f s 80.27% 76.35% 86.82%

(y) CNN M 1024 (C) f s 82.55% 74.85% 87.89%

(z) CNN M 128 (C) f s 82.90% 73.99% 88.13%

the accuracy of the system. Our experimental results

on three challenging real-world materials databases

show that the best performing pre-trained CNN ar-

chitectures can achieve up to 91.03% mean average

precision for cross-dataset scenarios. We also demon-

strate that synthesized data achieve an improvement

on mean average precision when used as training data,

which spans from ∼ 5% to ∼ 19% across three widely

used materials databases of real-world images and in

conjunction with pre-trained CNN architectures.

The rest of the paper is organised as follows.

Section 2 gives details of the material classiﬁcation

pipeline used for our experiments. Section 3 inves-

tigates whether synthetic data generalise better than

real-world images for the material classiﬁcation task

when applied as training datasets and in conjunction

with pre-trained CNN architectures and presents ex-

perimental results in this regard. Finally, conclusions

are given in Section 4.

2 MATERIAL CLASSIFICATION

PIPELINE

Every block in the material classiﬁcation pipeline is

ﬁxed except the feature extractor as different CNN

architectures (pre-trained on 1000 ImageNet classes)

are plugged in, one at a time, to compare their per-

formance utilizing the mean average precision (mAP)

metric. Given a training dataset T

consisting of m

material categories, a test dataset T

comprising un-

seen images of the material categories given in T

, and

a set of n pre-trained CNN architectures (C

,...C

), the

pipeline operates as follows: The training dataset T

is used as input to the ﬁrst CNN architecture C

. The

output of C

is then utilized to train m SVM linear

classiﬁers. Once trained, the test dataset T

is em-

ployed to assess the performance of the material clas-

siﬁcation pipeline using mAP. The training and test-

ing procedures are then repeated after replacing C

with the second CNN architecture C

to evaluate the

performance of the material classiﬁcation pipeline.

For a set of n pre-trained CNN architectures, the train-

ing and testing processes are repeated n times. Since

the whole pipeline is ﬁxed (including the training and

test datasets, learning procedure and evaluation pro-

tocol) for all n CNN architectures, the differences in

the performance of the material classiﬁcation pipeline

can be attributed to the speciﬁc CNN architectures

used.

Following (Chatﬁeld et al., 2014), we have cho-

sen three baseline CNN architectures for our experi-

ments, namely Fast (CNN-F), Medium (CNN-M) and

Slow (CNN-S). The CNN-F architecture is similar to

the one used by Krizhevsky et al. (Krizhevsky et al.,

2012). On the other hand, the CNN-M architecture

is similar to the one employed by Zeiler and Fergus

(Zeiler and Fergus, 2014), where as the CNN-S archi-

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

428

tecture is related to the ’accurate’ network from the

OverFeat package (Sermanet et al., 2013). All these

baseline CNN architectures are built on the Caffe

framework (Jia et al., 2014) and are pre-trained on

ImageNet (Deng et al., 2009). Each network com-

prises 5 convolutional and 3 fully connected layers for

a total of 8 learnable layers used to extract the mate-

rial features from the images presented. Since transfer

learning is applied, the CNN model output discussed

above refers to the activations of the fc6 fully con-

nected layer, which is the layer before the last. This

is applied to all three CNN architectures used in the

current pipeline. Data augmentation is used through-

out the paper as it provides informative samples from

current images for the system to use and increase the

amount of training data. For instance, if we take the

ﬁrst row in in Table 1, it means that the C+F augmen-

tation is used to generate the new images during the

training phase and is further combined with the sum

pooling in the testing phase (s). For further design and

implementation details for these architectures, please

see Table 1 in [15].

3 SYNTHETIC DATA VS

REAL-WORLD DATA FOR

TRAINING

For synthetic images, perfect segmentations are usu-

ally available without the need for manual segmen-

tation, and a huge number of them can be generally

obtained automatically. The system presented uses

patch segments to train our material system to iden-

tify the correct feature points of a selected category to

overcome the problems of material datasets that con-

sist of small fraction of possible real-world conditions

under a controlled laboratory environment. Recently,

it was shown by (Weinmann et al., 2014) that real-

world materials can be classiﬁed using synthesized

training data. In this work, we challenge the domi-

nant role of real-world images in the ﬁeld of material

classiﬁcation and methodically analyze how synthetic

data affects the results when applied as training sets

and used in conjunction with pre-trained CNN archi-

tectures. We use cross-dataset analysis in this sec-

tion to demonstrate how well the real-world and syn-

thetic training datasets perform in complex real-world

scenarios by employing real-world test datasets. The

synthetic data is generated using a different database

to the one used for training which will still ﬁt within

our system’s aim: to train on one dataset and test on a

different dataset.

Training m linear SVM classiﬁers, we take into

consideration for our analysis only the classes that

are common across both the synthetic and real-world

data. For instance, in this type of scenario, if syn-

thetic data is chosen for training and a real-world

dataset such as FMD is chosen for testing, then only

the four common classes such as fabric, leather, stone

and wood, are selected to be included in the analy-

sis. The same would be achieved for when real-world

database is chosen for training and synthetic for test-

ing.

3.1 Material Databases

Three different real-world databases are used in our

experiments: 1) Flickr Material Database (FMD)

(Sharan et al., 2010), 2) ImageNet7 dataset (Hu et al.,

2011) which was derived from ImageNet (Deng et al.,

2009) by collecting 7 common material categories,

and 3) MINC-2500 (Bell et al., 2015) which is a

patch classiﬁcation dataset with 2500 samples per cat-

egory. For synthetic data, University of Bonn dataset

(UBO2014) (Weinmann et al., 2014) is used for train-

ing purposes and tested against FMD, ImageNet7 and

MINC-2500 datasets. This synthetic dataset consists

of 7 material categories with 12 different item sam-

ples per category. Each material sample is measured

and consists of 151 diverse lighting directions and 151

viewing direction of the of it resulting in 22,801 im-

ages per category and 159,607 images in total. As ev-

ident, all three real-world databases consist of neither

the same number of images nor categories between

them. For this speciﬁc reason and in order to keep the

tests on a common base, we consider the ﬁrst half of

the images enclosed in each database category as pos-

itive training samples and the other half for testing.

Regarding negative training samples, for a category,

the ﬁrst 10% images of the remaining classes from

the dataset are collected together. Generating the neg-

ative training subset this way means the category still

has images from the same dataset, but they will be dif-

ferent from the evaluated category. Finally, a dataset

containing 1414 random images is utilized and kept

constant as the negative test data of our system for all

the experiments that follow. In total, 14 different vari-

ants of the baseline CNN architectures with different

data augmentation strategies are compared on FMD,

ImageNet7 and MINC-2500.

3.2 Cross-dataset Analysis with

Real-world Images

Results for three different cross-dataset experiments

are given in Table 1: 1) Training on FMD and test-

ing on ImageNet7 2) Training on FMD and testing

Material Classiﬁcation in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?

429

Table 2: Material classiﬁcation results using synthesized images.Training is performed using synthesized data from (Wein-

mann et al., 2014). Bold font highlights the leading mean result obtained when tested on databases of real-world images

(FMD, MINC-2500, ImageNet7).

Image Aug. MINC-2500 ImageNet7 FMD

Method

Training Testing mAP mAP mAP

(a) CNN F (C) f s 94.99% 95.41% 78.76%

(b) CNN S (C) f s 94.56% 95.61% 78.23%

(d) CNN M (C) f s 95.16% 95.19% 77.30%

(e) CNN M (C) f m 95.10% 95.11% 76.79%

(f) CNN M (C) s s 95.62% 96.17% 79.26%

(g) CNN M (C) t t 95.52% 96.20% 78.95%

(h) CNN M (C) f 94.24% 94.06% 70.91%

(i) CNN M (F) f 93.43% 95.64% 74.25%

(j) CNN M GS 91.58% 93.32% 63.17%

(k) CNN M GS (C) f s 94.77% 93.48% 70.34%

(l) CNN M 2048 (C) f s 95.42% 95.06% 77.01%

(m) CNN M 1024 (C) f s 94.89% 94.81% 69.84%

(n) CNN M 128 (C) f s 95.25% 93.49% 63.53%

Figure 1: Increase in Mean average precision (mAP) of the material classiﬁcation pipeline when UBO2014 database is used

as training dataset and testing is done on MINC-2500, ImageNet7 and FMD as compared to the results obtained when training

and testing datasets are both generated by using the same database of real-world images (e.g., training on FMD and testing on

FMD).

on MINC-2500 3) Training on MINC-2500 and test-

ing on ImageNet7. Considering the fact that FMD

dataset is quite small, with only 100 images per ma-

terial class, it performs better when used for training

with reduced feature dimensionality per image, also

observed in Zheng et al (Csurka et al., 2004). In Table

1, with FMD as training database, the material classi-

ﬁcation pipeline performs best in testing the overlap-

ping categories with ImageNet7 when Medium CNN

architecture is used with 128 feature points per im-

age extracted. The crop and ﬂip augmentation and

sum pooling collation is also used in this conﬁgura-

tion and an accuracy of ∼ 82% is achieved across the

metric used. For FMD as training and MINC-2500

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

430

as testing database, the material classiﬁcation pipeline

achieves the best accuracy in testing the overlapping

categories when CNN-M architecture is utilised with

2048 feature points per image extracted. Crop and

ﬂip augmentation and sum pooling are also used and

the resulting accuracy is ∼ 76% across the metric

used. It is evident from Table 1 that the performance

of the system increases when MINC-2500 is used as

training database and overlapping categories of Ima-

geNet7 are tested. This is due to the fact that MINC-

2500 database enables the use of more images for

positive training. When testing the overlapping cat-

egories with ImageNet7. In this case, the highest ac-

curacy is again achieved when CNN-M is used. How-

ever, only ﬂip is used as augmentation and no colla-

tion is utilised with this CNN architecture as opposed

to the above two cases. The resulting accuracy of the

system is ∼ 90%. This is the case of ﬁnding the best

balance before over-ﬁtting occurs. Finally, the result-

ing average across all three experiments is ∼ 82%.

3.3 Cross-dataset Analysis with

Synthesized Images

By using the UBO2014 database (Weinmann et al.,

2014) for training, there is considerable increase in

performance of the material classiﬁcation pipeline as

compared to when trained on databases with real-

images as shown in Table 2. In the case of us-

ing UBO2014 for training and MINC-2500 for test-

ing, there is a ∼ 19% increase in performance, com-

pared to when FMD was used for training, giving

an accuracy of ∼ 95%. Testing on ImageNet7 also

gives UBO2014 the edge over FMD and MINC-2500.

When comparing with FMD as test database, the in-

crease in performance is quite signiﬁcant at ∼ 14%.

This can be attributed to the fact that FMD is a small

size real-world dataset that provides limited training

and testing data, whereas UBO2014 covers a large

fraction of viewing and illumination conditions on

material appearance as well as the intra-class variance

of the materials. This outlined reason for the signif-

icant increase in performance shows the importance

of the amount of training data as well as the necessity

of having bigger deﬁned existing real-world datasets.

When comparing UBO2014 training to MINC-2500

training, an increase in performance of ∼ 5% is also

observed which gives the synthetic data a better gen-

eralization in all tests and classes of materials for the

datasets we used. Finally, Fig. 1 shows the increase in

mean average precision (mAP) of the material classi-

ﬁcation pipeline when UBO2014 database is used as

training dataset and testing is done on MINC-2500,

ImageNet7 and FMD as compared to the results ob-

tained when training and testing datasets are both gen-

erated by using the same database of real-world im-

ages (e.g., training on FMD and testing on FMD). It

is thus clear from Fig. 1 that training with synthetic

data can even surpass the performance that is achieved

by training on the same database of real-world images

on which it is tested.

4 CONCLUSIONS

We have done rigorous investigation of the effects of

training data type for the task of material classiﬁca-

tion in the wild utilizing pre-trained CNN architec-

tures. Our cross-dataset experiments have demon-

strated that synthetic training data generalises bet-

ter for material classiﬁcation than real world train-

ing data. It seems that synthetic data is a promis-

ing approach to overcome the dataset bias problem

of datasets collected from real images. It will be an

interesting future direction to investigate if synthetic

data can be combined with real images to improve ac-

curacy and generalisation abilities of CNNs.

5 ACKNOWLEDGMENTS

This work was supported in part by the UK Engineer-

ing and Physical Sciences Research Council EPSRC

[EP/R02572X/1 and EP/P017487/1] and the UK Eco-

nomic and Social Research Council [ES/M010236/1].

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