# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 3 Experiment
### Visual Comparisons  
To demonstrate the advantage of our proposed algorithm, some images with complex backgrounds are shown in Figure 12. We compared our method with state-of-the-art approaches on the ASD, ECSSD, DUT-OMRON, PASCAL-S datasets.  
As Figure 12 shows, the most salient region detection methods can effectively manage cases with relatively simple backgrounds and homogenous objects. However, these methods fail to manage the complicated cases, especially when the salient object and the background are similar with each other. In contrast, our method can deal with these intricate scenarios more effectively. We compare these methods in the following two aspects: (1) the effectiveness of the background suppression; (2) the integrity and uniformity of the salient objects. First, as shown in Figure 12, only one low level cue cannot effectively suppress the background regions, such as the global contrast-based method RC [7], which can be proved with the second image in DUT-OMRON dataset. When the salient regions are compacted, SF [8] which utilizes the compactness cue can have a good performance, such as the fourth image in ASD dataset. However, SF may wrongly suppress the salient regions. In addition, the background-based method RBD [11] cannot always suppress the background effectively, such as the third image in DUT-OMRON dataset and the second image in PASCAL-S dataset. TLSG [16] which generated the saliency map with the compactness cue and diffused the saliency map on 2-layer sparse graph can obviously improve the performance in some cases, such as the second and third images in DUT-OMRON dataset. However, there still exists some cases that it cannot manage effectively, such as the first images in ECSSD dataset and PASCAL-S dataset. HS [41] which exploited the hierarchical  
Figure 12: Visual comparison of state-of-the-art approaches to our method on four datasets.  
saliency can obtain a good performance, especially when the salient objects are small, such as the second image in ASD dataset. But it cannot well suppress the background regions, such as the third images in DUT-OMRON dataset. Relatively, the diffusion-based methods BSCA [30], GBMR [23], MC [24] can obtain a homogeneous salient region. But when the salient objects are heterogeneous and the background regions are cluttered, they cannot always highlight the salient objects completely and uniformly, in some cases, they even wrongly highlight the background regions, such as the second images in ASD dataset and DUT-OMRON dataset. However, our method can manage these complicated scenarios effectively. Especially, when the salient objects are not compacted, our method can also highlight the salient region uniformly but slightly incompletely, such as the third images in the PASCAL-S dataset. What is more, our method shows the well robustness to the scale variations of the salient objects. When the salient objects are relatively small, our method can still have a good performance, which can be seen in the first and fourth images in DUT-OMRON dataset. In some scenes, the saliency maps generated by our method can almost be the same with the ground truth.