## 3 Experiments
### Parameters Selection  
In the task of texture detection, FHOG feature can be extracted with different cell sizes, so it is necessary to find the optimal cell size through ablation experiments.  
Table 1 shows the results of an ablation experiment to find the best cell size in feature extraction. As shown in Table 1, texture detection with the cell size of \(16\times 16\) pixels has the highest precision and recall rate (88.24% and 96.4%, respectively), although they are close to the results when the cell size is \(32\times 32\) pixels. However, in terms of computational complexity, when the cell size is \(16\times 16\) pixels, the detection time is the shortest (0.333 seconds). This detection time is 0.108 seconds faster than the time when the cell size is \(32\times 32\) pixels. We concluded that bigger cells perform better in precision than the smaller ones until the size has reached \(16\times 16\), while the time consumption decreases as cell size increases except the size \(32\times 32\). Therefore, size \(16\times 16\) is the best choice.  
In the task of saliency detection, ablation experiments are performed for the two tricky problems:  
1. what is the best weight of global saliency map in the fusion operation;
2. how to solve the problem of high time consumption caused by matrix decomposition.  
For the first problem, we made a comparison of the results of saliency detection for different global-map weights. As shown in Figure 8, Precision peaks at the weight value of 0.4, and the Recall remains high at this value. Although the time consumption is very small when the weight is 1 or 0, its Precision and Recall are extremely low. Consequently, the optimal weight value of global saliency map is 0.4.  
To solve the problem of time consuming in matrix decomposition, initial saliency maps are down-sampled before global decompositions, as smaller matrices make decompositions faster. Note that the initial saliency maps are not necessary to resize for local decompositions. We make a comparison of the results for different map sizes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell size** & **Time (s)** & **P (\%)** & **R (\%)** \\ \hline \(4\times 4\) & 0.698 & 81.1 & 96.1 \\ \(8\times 8\) & 0.502 & 84.2 & 95.9 \\ \(16\times 16\) & **0.333** & **88.24** & **96.4** \\ \(32\times 32\) & 0.441 & 88.5 & 96.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results of texture detection for different cell sizes.  
As shown in Table 2, the smaller size for global decompositions not only makes the whole detection task have less time consumption, but also makes it have higher precision and recall rates. When the size is \(48\times 48\), all of the three parameters are optimal values, i.e., the highest precision rate (94.29%) and recall rate (94.29%), and the lowest time consumption (0.415 seconds). Note that the detection time of size \(48\times 48\) is 18.335 seconds faster than that of size \(350\times 350\), and the precision and recall rate are respectively 9.59% and 15.19% higher than those of size \(350\times 350\). The possible reason is that when an image is down-sampled, the calculated data is reduced, and some noises and redundant regions are removed as well.