## 4 Experimental Results
### Performance Evaluation. Firstly, we use a fixed threshold, which ranges from 0 to 255, to produce the binary object masks on the saliency maps and compute the precision rate and recall rate with the corresponding ground truth masks using Equations (12) and (13). Then, we determine the Precision-Recall curves of our method compared with the other five methods, all of which are shown in following Figure 10.  
We can clearly see in Figure 10 that the performance of our method is significantly better than that of the FASA method [6]. The precision of our method always remains above 90% when the recall ranges from 0 to 70%. Our method is better than the RC method when the recall rate of RC remains between 0 and approximately 87%, and they are not much different when the recall is between 87% and 100%. Compared with the SF method, our method's precision is always higher for recall rates ranging from 0 to 72%, after which our method's precision begins to slightly decrease for recalls between 72% and 100%. Secondly, we evaluate our method based on the adaptive threshold [7] by binarizing the salient maps. The mean saliency of the salient map is illustrated as follows:  
\[\text{\it Sal}_{mean}=\frac{2}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}Sal(i,j) \tag{16}\]  
Figure 10: Precision-recall curves of our method compared with the other five methods. We calculate the average precision, the average recall and the average \(F_{\beta}\)-measure as the overall performance, and then compare with the corresponding ground truth binary masks obtained using the same adaptive threshold. The performance results are shown in Figure 11 and Table 1. The average precision of our method is 89.07%, and the average recall and \(F_{\beta}\)-measure are 72.65% and 83.17%, respectively. The following figure and table clearly show that the performance of our method is much better than that of the other methods. Additionally, we calculate the MAE between the salient maps and the binary ground truth based on Equation (15). The result is shown in Table 2 and Figure 12.  
Table 2 shows that our method's MAE, which is 14.17%, is the second lowest among the six methods. This error is only higher than that of the SF method by approximately 1.07%, and it is lower than that of the FAST method by approximately 0.57%. The MAE rate of RC is 23.81%, which is almost twice that of our method. Therefore, our method is found to perform well when considering the much lower relative MAE. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Average Precision** & **Average Recall** & **Average _F-measure_** \\ \hline RC & 0.882109 & 0.598936 & 0.717257 \\ HC & 0.742677 & 0.732207 & 0.715192 \\ FT & 0.748497 & 0.591377 & 0.667423 \\ FASA & 0.844401 & 0.743692 & 0.796570 \\ SF & 0.884026 & 0.741832 & 0.830650 \\ Ours & 0.890686 & 0.726538 & 0.831714 \\ \hline \end{tabular}
\end{table} 
Table 1: Average precision, recall and _F_-measure. Figure 11: The average Precision, Recall and _F_-measure based on the adaptive threshold. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **RC** & **HC** & **FT** & **FASA** & **SF** & **Ours** \\ \hline Mean absolute error & 0.2381 & 0.1749 & 0.2056 & 0.1474 & 0.1309 & 0.1417 \\ \hline \end{tabular}
\end{table} 
Table 2: The Mean absolute error to ground truth. Moreover, we compared the average time taken by each method on a Dual Core 2.3 GHz machine with 8 GB of RAM. The execution time of our method implemented in C++ for producing one salient map on MSRA-1000 datasets was estimated as approximately 5.63 s on average. For the other five state-of-art methods, namely RC, HC, FT, FASA and SF, we used the authors' implementations in C++. Although our method is slower than FASA (0.028 s) and slightly slower than SF (5.23 s) and RC (5.20 s), the saliency detection accuracy is improved in our method, and the time consumed for processing is acceptable. Note that most of the time is consumed in the LTSCH building step, such as texture feature extraction and superpixels segmentation.