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Selective Visual Attention: Computational Models and Applications by Weisi Lin, Liming Zhang

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5.2 Hierarchical Object Search with Top-down Instructions

Most visual attention models are based on low-level features that may not concern a complete object and they aim to find some salient locations namely space-based saliency. However, in some scenes these salient locations may not represent any significant object. In the other words, salient locations are probably speckle noise or blemishes on the image that is unrelated to any interesting object. A lot of recent literature [35–40] suggests an object-based attention to directly locate significant objects. For an example of object-based attention, two overlapped objects or a blocked object in a scene, which are difficult to pop out in space-based attention, can still draw observers' attention in some cases. In addition, one object probably has very complex structure: several features constituting the same object or an object group maybe includes several small objects. In that case, space-based visual attention will not be effectual. For example, some salient locations extracted from these low-level features do not contain any significant object, and thereby object-based visual attention models and the models integrating both object-based and location-based attentions have been proposed in the literature of psychophysics and computer vision [35–38].

A hierarchical object search model [13] proposed in 2003 is a typical object-based computational model with top-down instructions of a simple binary code flag. All competition in ...

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