## 3 Dataset and Feature Description
### Feature Description
#### 3.2.4 Uniform Local Binary Pattern  
LBP, which was first introduced by [PERSON] et al. [51], is a useful texture-based feature. It mainly captures the edge properties by taking the intensity differences of the center pixel with its surrounding pixels. In this work, we have considered a window size of \(3\times 3\) around a center pixel. As a result, a total of eight neighboring pixels were considered. The difference between the center pixel and each of the surrounding pixels were calculated. If the difference was greater than zero, we assigned 1; else 0. In this way, each center pixel was represented in a two-digit LBP code. The calculation of the LBP code is shown in Figure 3. In this figure, the top left corner is taken as the 7 th bit and the bits were considered in a clockwise fashion until it reaches to the 0 th bit. The resultant 8-bit binary number is formed which is then converted to its equivalent decimal number. This process is formulated in Equation (6), where, \((x_{ccn},y_{ccn})\) is the center pixel and \(i_{k}\) is one of the surrounding pixels.  
\[LBP(x_{ccn},y_{ccn})=\sum_{k=0}^{7}new(i_{ccn}-i_{k})2^{k} \tag{6}\]  
A transition in a binary string is defined as the change from 0 to 1 or change from 1 to 0. The strings that comprise less than or equal to two transitions are known as uniform strings and others are called as non-uniform strings. The main purpose of using uniform pattern was to eliminate the redundant features and capture the information properly. In this work, uniform property was incorporated with the binary strings obtained by LBP. Therefore, most of the redundant binary strings will be eliminated as they were non-uniform. The obtained uniform strings were converted to their respective decimal values. The histogram of those values were taken as the feature vector as formulated in Equation (7), where, \(L\) denotes the number of bins.  
\[Hist_{n}=\sum_{x,y}I(Image_{label}(x,y)=i),\ \ i=0,\ldots,\ L-1 \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Dimension** & **Feature Dimension** & **Final Feature Dimension after down Sampling** \\ \hline \(32\times 32\) & \(40,960\)\((=40\times 32\times 32)\) & \(640\)\((=\frac{40,960}{85\times 3})\) \\ \(48\times 48\) & \(92,160\)\((=40\times 48\times 48)\) & \(1440\)\((=\frac{92,160}{85\times 3})\) \\ \(64\times 64\) & \(163,840\)\((=40\times 64\times 64)\) & \(2560\)\((=\frac{16,840}{80\times 8})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation of feature vector size of Gabor filter bank (applied in 8 different orientations and 5 distinct scales) for various image dimensions with a down sampling factor of 8.  
Figure 3: Clockwise direction gives \((01110100)_{2}=(116)_{10}\) as the center pixel after local binary pattern (LBP) computation.  
In this work, initially all the images were divided into \(16\ (=4\times 4)\) blocks irrespective of the image dimension. The main purpose of the blocking was to preserve the local information of the image. Divisions were made uniformly in the entire image. Then uLBP was applied in each sub blocks. The obtained feature vectors from each sub block were concatenated to get the final feature vector. Here, the feature dimension of uLBP was \(59\ (=8*(8-1)+3)\). As the image was divided in \(16\) sub blocks, the final feature dimension becomes \(944\).