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Detecting multiple salient objects in complex scenes is a challenging task.
Meanwhile, if multiple salient objects have the comparable local contrast, our method also can detect these objects.
In order to detect multiple salient objects of different sizes in a scene, the saliency of a pixel is estimated via its saliency support region which is defined as the most salient region centered at the pixel.
The proposed method finds the saliency support region for a local area and computes the saliency of this region with respect to its surroundings to detect multiple salient objects adaptively.
Experimental results demonstrate that the proposed algorithm detects multiple salient objects faithfully while suppressing the background successfully, and it yields a significantly better performance of panorama saliency detection compared with the recent state-of-the-art techniques.
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Although some methods preset multiple scales, such as IT and AC, they cannot effectively detect salient objects in the complex scenes.
The gradient-based global feature represents the rough locations of salient objects in an image, and the seam locations obtained using multiple layers are better than those obtained using a single layer.
On the contrary, the newly hatched birds are attracted toward salient objects, particularly ones that move.
Lewis says that it is the salient objects.
In combining them, salient objects appearing strongly in a few maps may be masked by noise or less salient objects present in a larger number of maps.
It is challenging because most of the salient objects share similar depth as the background.
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