基于物体的选择性注意在心理学领域正日益为广大研究人员所认可,而计算机视觉领域中现有的注意模型大多数是基于特征的,或者是基于空间的.本文给出了一种基于物体的选择性注意计算模型.该模型将“感知物体”作为引起注意的基本单元,并给出了感知物体及其邻域的定义.该注意模型包括两个步骤：（1）在给定图像中选择第一个注视点;（2）在整幅图像中实现注视点的有效转移.在该注意模型中,感知物体与其邻域之间灰度值的绝对差异--对比度,被作为该感知物体显著性的一种度量,并且注视点在图像中的转移顺序是由每个感知物体的显著度的次序来决定的.该模型的优点有：首先,由于该模型是完全基于感知物体的,使得其输出结果可以很容易地应用到物体识别、图像分割和场景分析中;其次,该模型是多尺度的,也就是说,它可以根据实际任务的需要进行适当的调整.大量的真实图像实验表明,所提出的模型具有一定的合理性.

The object-based selective attention has been given increasingly importance in psychology domain in recent years,whereas most of the existing models of selective attention in computer vision field are either feature-based or space-based.In this paper,an object-based computational model of selective attention is proposed where "perceptual object" is postulated as the basic attention unit.The proposed attention model consists of following two steps:(1) how to select the first focus of attention in a given image;(2) how to shift the attention within the whole image.Under this model,the contrast,defined as the absolute gray difference between the "perceptual object" and its neighborhood,is used as the measure of the object's salience,by which the attention is determined and shifted.The advantages of this model lie in: Firstly,the model is entirely based on perceptual objects,its results can be easily applied to object detection,image segmentation,and scene analysis.Secondly,the model is of multi-scale,flexible enough to be easily adjusted according to the specific applications.Extensive experiments on real images show that the proposed attention model works reasonably.