## 4 Experiment Results and Analysis
### Qualitative Evaluation  
We visually compared the proposed saliency cuts method with two other state-of-the-art saliency cuts methods: adaptive triple thresholding for saliency cuts using Otsu automatic thresholding (ATT_Cuts) [16] and saliency cuts using fixed thresholding (RC_Cuts) [17]. In Figure 9, the first row displays typical input images such as people and objects. Ground truth images are shown in the second row. The third row shows the saliency maps of the natural scene images of the first row. The fourth and fifth rows are saliency cuts using the Otsu algorithm and global fixed thresholding, respectively. The last row shows the results of the proposed saliency cuts method. As shown in Figure 9, the results of the proposed method for saliency map binarization were close to ground truth images. The proposed saliency cuts technique extracted salient objects even when the background and foreground regions had very similar information. In comparison, the fixed thresholding method can extract only a single salient object from a given image and may fail to segment salient objects when pixels in the image had low saliency values. Saliency cuts using the Otsu method has a similar drawback of misclassifying a salient object as a background region when the saliency values of a given image are low. However, it can detect multiple objects if the saliency values of pixels are sufficiently high. Comparatively speaking, the proposed approach can reduce these drawbacks; it can extract salient objects with a lower amount of data about the saliency map without misclassifying them as background regions, as well as even being able to detect multiple salient objects.  
Figure 8: Example of using the masking method: (**a**) input image, (**b**) saliency map, (**c**) the proposed saliency cuts, (**d**) full-color space salient objects, (**e**) outer and inner edges.  
The results of boundary detection together with a comparison between the proposed method and other well-known methods are shown in Figure 10. The first row displays given images, such as people, birds, and different kinds of objects. The second and third rows show the saliency maps and ground truth generated from the images in the first row, respectively. The other saliency cuts methods such as RC_Cuts and ATT_Cuts are illustrated in the fourth and fifth rows. The results of saliency cuts using the proposed method are shown in the sixth row, and the last row displays the boundaries of salient objects. Experimental results show that in many cases the proposed method extracted objects more accurately than other methods. Moreover, our approach worked effectively, even when there were multiple objects in natural scene images, as illustrated in Figure 10.  
However, the proposed method may fail to segment a salient object that has a very low saliency value and contains very similar foreground and background regions, as shown in Figure 11.  
Figure 10: Experimental results of outer contour generation. From top to bottom rows: (1) input images, (2) saliency maps (grayscale images), (3) ground truth images, (4) ATT_Cuts results, (5) RC_Cuts results, (6) results of the proposed method, (7) boundary detection using the Canny edge technique.  
Figure 9: Visual comparison between saliency cuts methods. From top to bottom: (1) input images, (2) ground truth, (3) saliency maps, (4) ATT_Cuts [16], (5) RC_Cuts [17], (6) the proposed method.