Material Textures

Material texture datasets can be similar to some of the natural texture datasets. Like natural image datasets, they often contain multiple images for each class, they are popular in texture classification tasks. Material texture datasets are the most preva- lent type within the world of texture databases.

The CUReT (Columbia-Utrecht Reflectance and Texture) database comprises images of 61 materials (classes). Each material has been photographed from a range of orientations but with a single light source. There is a black background around most samples and the mechanism/stand on which the photographed materials are placed is visible in some of the images (Dana et al., 1999). The large number of samples per class makes this a good database for classification tasks and it is used by a large number of researchers (Guo et al., 2010, Hayman et al., 2004, Liu and Fieguth, 2012, Varma and Zisserman, 2003, Zhang et al., 2006). Figure 2.5 shows some examples from the CUReT database.

The KTH-TIPS (KTH Textures under varying Illumination, Pose and Scale) database (Fritz et al., 2004) is composed of 10 classes. Each class contains 81 images taken with varying lighting directions (3 Illumination), angles (3 Poses) and magnification (9 Scales) (3x3x9 = 81). The KTH-TIPS has an advantage over CUReT in that the images have very little to no non-texture borders. Because of their variety in the IPS dimensions, they are very popular for feature identification and classification tasks (Crosier and Griffin, 2010, Hayman et al., 2004, Nowak et al., 2006, Zhang et al., 2007). Figure 2.6 shows some examples from the KTH-

(a) Eight distinct CUReT materials

(b) Eight distinct views of a CUReT texture

Figure 2.5: Eight CUReT textures (a) and eight views of CUReT sample 08-01

TIPS texture database.

KTH-Tips2 extends KTH-TIPS to include 432 samples for 11 texture classes. Mallikarjuna and Targhi (2006) provide details on the Illumination, Pose and Scale variation used for each class, as well as details on post-processing, such as cropping boundaries and the methods used for dealing with poor quality images.

Photex contains images of the same surface taken at 40 different illumina- tion directions (Various, 1999). It is used for classification tasks in Drbohlav and Chantler (2005), Targhi et al. (2008). There is also a 3D version of the Photex which includes real surface rotation as opposed to image rotation. Photex comprises 1680 images of 30 textured materials. This dataset is popular with shape from texture methods (Dong and Chantler, 2004, Dong et al., 2007, Jian and Dong, 2011). Fig- ure 2.7 shows some examples from the Photex dataset.

(a) Eight distinct KTHTips materials

(b) Eight distinct views of orange peel from KTHTips database

Figure 2.6: Eight KTH-Tips textures (a) and eight views of orange peel from KTH-Tips

ages with 40 gray-scale images per class of varying viewpoints and zoom levels. This is quite a challenging dataset because the illumination conditions are not con- trolled and also because the images have been taken using a non-calibrated amateur camera (Various, 2006). Image categories include several types of bark, wood, brick, carpets, textiles, stone, as well as water. The fact that each image shows a unique texture and the classes of image are well curated, make this a very good dataset for both identifying texture features (de Siqueira et al., 2013, Xu et al., 2012, 2010) and evaluating their performance in classification tasks (Ji et al., 2013, Kong and Wang, 2012, Liu et al., 2011, Quan et al., 2014).

(a) Eight distinct PhoTex materials

(b) Eight distinct views of one PhoTex image

Figure 2.7: 8 PhoTex textures (a) and eight views of one PhoTex texture.

The Outex dataset (Ojala et al., 2002a) includes both natural images and ma- terial textures. This dataset contains images of 320 textured surfaces organised into 29 classes. The dataset is very large as each of the 320 textured surfaces was imaged under three different types of light source, nine rotations and six different resolu- tions. There are therefore 51,840 samples, in both RGB and gray-scale. Like many other material datasets, it is primarily used to evaluate the performance of texture features in classification tasks (Arvis et al., 2011, M¨aenp¨a¨a and Pietik¨ainen, 2004, Ojala et al., 2002a,b, Ojansivu and Heikkil¨a, 2008).

The UIUC database (Lazebnik et al., 2005b) contains 40 gray-scale images for each one of 25 classes, with variation in magnification and viewpoint. This dataset also suffers variation due to uncontrolled illumination conditions. It is considered a challenging database for classification due to the large variations in viewpoint

and magnification in the 40 images per class (Varma and Garg, 2007). The UIUC dataset is most often used in evaluating the performance of textural features for classification (Lazebnik et al., 2005a, Mellor et al., 2008, Sifre and Mallat, 2013, Zhao et al., 2012).

The MEASTEX (MEASurement of TEXture) database contains a combination of images of homogeneous-natural and artificial materials (Smith and Burns, 1997). They are divided into three broad categories, namely grass textures, materials tex- tures and surface textures. The grass category contains different types of grass in a range of presentations. The materials category contains images of various classes of mulch, sandstone, gravel, pebbles and sand. The surfaces category is divided into various types of asphalt, concrete, corrugated iron and office partitions.

There are many other material databases available in the literature, such as the The PSU-NRT (PSU Near Regular Texture) dataset (Liu et al., 2004b) and the ALOT (Amsterdam Library Of Textures) (Geusebroek and Burghouts, 2009) which are diligently reviewed in Hossain and Serikawa (2013) and Bianconi and Fern´andez (2014).