# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 5 Experiments and Discussion
### Image Datasets  
Six benchmark datasets, consisting of one face image dataset (Face-95 [43]), one object image dataset (ETHZ [44]), one landmark image dataset (ZuBuD [45]), two color textural image datasets (KTH-2a [46] and VisTex [47]), and one natural scene image dataset (Corel-100 [48]), are summarized in Table 1 to evaluate the effectiveness, robustness, and practicability of the proposed descriptors.  
Figure 5: Schematic diagram of the proposed retrieval system.  
The Facet-95 (No. 1) is a face image dataset, and it was captured by a S-VHS camcorder. The Face-95 consists of 1440 images in 72 persons. Each person contains 20 images in JPG format with size of \(180\times 200\). Note that all images are with face expressions, illumination changes, head scales and head turns. In Figure 6a, there are some samples from Face-95.  
The ETHZ (No. 2) is an object image dataset, and it was taken with a Sony XC-77P camera. The ETHZ has 265 images in 53 natural objects. For each object, there are 72 images in BNG format with size of \(320\times 240\). Specially, each object is rotated by an arbitrary degree, so the ETHZ also can be used to evaluate the robustness of rotation. In Figure 6b, some samples from ETHZ are displayed.  
The ZuBuD (No. 3) is a landmark image dataset, and it was produced by the Panasonic-NV-MX300 and Pentax-Opti430 cameras. The ZuBuD has 1005 images in 201 landmarks. Each landmark contains  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Name** & **Image Size** & **Class** & **Images in Each Class** & **Images Total** & **Format** \\ \hline
1 & Face-95 & \(180\times 200\) & 72 & 20 & 1440 & JPG \\
2 & ETHZ & \(320\times 240\) & 53 & 5 & 265 & BNG \\
3 & ZuBuD & \(640\times 480\) & 201 & 5 & 1005 & JPG \\
4 & VisTex & \(128\times 128\) & 40 & 16 & 640 & PPM \\
5 & KTH-2a & \(200\times 200\) & \(11\) & 396/432 & 4608 & BNG \\  & & \(384\times 256\) & & & & \\
6 & Corel-1000 & or & 10 & 100 & 1000 & JPG \\  & & \(256\times 384\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of six image datasets.  
Figure 6: Some samples from six datasets: (**a**) Face-95; (**b**) ETHZ; (**c**) ZuBuD; (**d**) VisTex; (**e**) KTH-2a; and (**f**) Corel-1000.  
five images in JPG format with size of \(640\times 480\). Note that not only some occlusions (e.g., tree and car) are purposely included in images, but also all images are captured under different views points, weather conditions and seasons. Thus, the ZuBuD can be used for evaluating the effectiveness under the complex environment. In Figure 6c, some samples from ZuBuD are showned.  
The VisTex (No. 4) and KTH-2a (No. 5) are two color textural image datasets. On one hand, the VisTex (No. 4) was acquired by collecting images from videos and photographs. The VisTex is having 640 images in 40 categories (e.g., fabric, flower, matel, terrain, bark, sand, leave, stone, and so on). Each category includes 16 images in PPM format with size of \(128\times 128\). In Figure 6d, there are some samples from VisTex. On the other hand, the KTH-2a (No. 5) was taken with an Olympus C-3030 ZOOM camera, and it consists of 4608 images in 11 categories (e.g., cotton, brown bread, wood, wool, white bread, corduroy, linen, cracker, cork, aluminium foil and lettuce leaf). Each category contains 396/432 images in BNG format with size of \(200\times 200\). Specially, the KTH-2a also can be applied to evaluate whether the proposed descriptors are robust against rotation, scaling and illumination. In Figure 6e, there are some samples from KTH-2a.  
The Corel-1000 (No. 6) is a natural scene image dataset, and it was collected by the SIMPLcity system. The Corel-1000 has 1000 images in 10 natural scenes. Each natural scene contains 100 images in JPG format with size of \(384\times 256\) or \(256\times 384\). In Figure 6f, some samples from Corel-1000 are presented.