显著对象检测是视觉注意机制的一个重要应用基础研究，对于图像检索、场景分析、图像标注与对象识别都有着重要的研究意义。基于Tresiman特征整合理论和Koch计算框架，提出一种自然场景中视觉显著对象的检测方法。该方法首先建立适用于彩色自然场景的视觉显著度模型，计算多种不同特征的显著度，然后在融合不同特征的综合显著度图中提取显著对象。实验结果表明，与经典的Itti模型相比，这种方法不仅检测快速而且更准确地将视觉显著对象从背景中分离出来，更符合人眼的真实视觉注意过程。

Visual salient objects detection is an important fundamental application research of visual attention mechanism. It plays an important role in image retrieval, scene analysis, image annotation and object recognition. This paper proposes a novel approach for visual salient objects detection in natural scenes based on Treisman’s feature integration theory and Koch’s framework. In this approach, a visual saliency model for colored natural scenes is proposed and different feature saliencies are considered and computed. Then an effective method is given to extract salient objects from an integrated saliency map which is combined by different feature saliency maps. Comparing with Itti’s model, the experimental results indicate that not only the detection speed of this approach is faster, but also this approach can separate visual salient objects from their backgrounds more accurately and more efficiently. From this aspect, the approach in this paper is more similar to human’s real visual attention process than Itti’s model.