# Trademark Image Similarity Detection Using Convolutional Neural Network
## 4 Experiments and Results
### Experiments
#### 4.1.2 VGG-16 Features Extractor  
All of the following experiments were conducted based on the features extracted by VGG-16. 1. VGG-16-based similarity detection (VSD) with image scaling-up: This experiment utilized the VGG-16 model to extract all of the images' features. Moreover, all images were scaled to the size required by a VGG-16, which is equal to 224 \(\times\) 224. 2. VSD with image scaling-up and feature reduction using PCA: As mentioned above, the dimension of the extracted features should be reduced. So, this experiment used features reduced by PCA of scaling images. 3. VSD with image padding: In this experiment, each image was padded into a 224 \(\times\) 224 image, which is the input size required by VGG-16. 4. VSD with image padding and features reduction using PCA: As with the previous experiments, this experiment investigated the effect of the feature reduction technique for the features extracted from the padded images. Table 1 shows the average and normalized average ranks for each conducted experiment using the ResNet-50 features extractor and VGG-16 features extractor, respectively. The above table shows that Exp#1 and Exp#2 achieved results that are close to each other; the average rank and R of the related experiment were improved by applying the PCA reduction technique. In contrast, the worst results were obtained from the features extracted by the VGG-16 pooling layer for padded images. Table 2 presents the mAP value for each experiment. As shown in Table 2, the mAP values of the first four experiments were close to each other, which represent the experiments that utilized ResNet-50 as a feature extractor. Hence, the ResNet-50 feature extractor overcomes the VGG-16 performance in the conducted experiments. Figure 4 presents some examples of the developed trademark similarity detection system. The green rectangles identify the true positives (images belong to the same query class); the remaining images are bounded into red rectangles. A yellow background was used to clearly show the results. Table 3 presents the comparison between the performance of the proposed approach and other studies other previous studies that use similar approaches, where they used the same dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Experiment** & **Average Rank** & **Normalized Average Rank** \\ \hline Exp\#1 & RSD with image scaling & 776.699 & 0.0739 \\ \hline
**Exp\#2** & **RSD with image scaling and PCA** & **775.447** & **0.0737** \\ \hline Exp\#3 & RSD with image padding & 1106.663 & 0.107 \\ \hline Exp\#4 & RSD with image padding and PCA & 1102.505 & 0.105 \\ \hline Exp\#5 & VSD with image scaling & 1090.606 & 0.104 \\ \hline Exp\#6 & VSD with image scaling and PCA & 1070.84 & 0.102 \\ \hline Exp\#7 & VSD with image padding & 1398.403 & 0.134 \\ \hline Exp\#8 & VSD with image padding and PCA & 1382.158 & 0.132 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result of the average rank and normalized average rank for each experiment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Experiment** & **Mean Average Precision** \\ \hline Exp\#1 & 0.757 \\ \hline Exp\#2 & **0.759** \\ \hline Exp\#3 & 0.753 \\ \hline Exp\#4 & 0.753 \\ \hline Exp\#5 & 0.356 \\ \hline Exp\#6 & 0.357 \\ \hline Exp\#7 & 0.365 \\ \hline Exp\#8 & 0.365 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean average precision results for each experiment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Average Rank (AR)** & **Normalized Average Rank (NAR)** \\ \hline [PERSON] et al., 2017 [21] & 79,538.5 & 0.086 \\ \hline [PERSON] et al., 2018 [30] & - & 0.083 \\ \hline [PERSON] et al., 2021 [31] & - & 0.110 \\ \hline Proposed approach & 67,067.788 & 0.0725 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the proposed approach and sate-of-the-art approaches. [PERSON] et al. [21] used pre-trained models with different parameters over the METU dataset, and the output of FC7 of VGG16 achieved the best retrieval performance, which was 0.086 of NAR.