目的 图像的显著性检测在计算机视觉中应用非常广泛，现有的方法通常在复杂背景区域下表现不佳，由于显著性检测的低层特征并不可靠，同时单一的特征也很难得到高质量的显著图。提出了一种通过增加特征的多样性来实现显著性检测的方法。方法 在高层先验知识的基础上，对背景先验特征和中心先验特征重新进行了定义，并考虑人眼视觉一般会对暖色调更为关注，从而加入颜色先验。另外在图像低层特征上使用目前较为流行的全局对比度和局部对比度特征，在特征融合时针对不同情况分别采取线性和非线性的一种新的融合策略，得到高质量的显著图。结果 在MSRA-1000和DUT-OMRON两个公开数据库进行对比验证，实验结果表明，基于多先验特征与综合对比度的图像显著性检测算法具有较高的查准率、召回率和F-measure值，相较于RBD算法均提高了1.5%以上，综合性能均优于目前的10种主流算法。结论 相较于基于低层特征和单一先验特征的算法，本文算法充分利用了图像信息，能在突出全局对比度的同时也保留较多的局部信息，达到均匀突出显著性区域的效果，有效地抑制复杂的背景区域，得到更加符合视觉感知的显著图。

Objective Saliency detection is widely used in computer vision. When dealing with simple images, the bottom-up low-level features can achieve good detection results. As for images with complex background, the existing methods do not perform well and many regions of background could also be detected, and since the low-level features of saliency detection are not so reliable. At the same time, a single feature is also difficult to get high-quality saliency map. Hence, more salient factors are need to be integrated to solve it. This paper proposes a method to achieve saliency detection by increasing the diversity of features. Method A new consistency method base on the standard structure of the cognitive vision model. On the basis of high-level prior knowledge, the background prior characteristics and the center prior characteristics are redefined. By combining the theory of boundary prior and merging the spatial and color information get background prior saliency map. Then, according to the mechanism of human visual attention, taking the center of the background prior map as the central position of the salient region, and then apply the center prior, get the center prior saliency map. And consider the human eye vision to pay more attention to the warm color, while the warm tone has an effect on the image saliency, thus adding color prior. The local contrast method is better for the detailed texture of the image, but the integrity is not enough, the saliency map is generally dark. The contrast between the salient region and the background region is not enough, and it does not highlight the overall sense of the saliency objects. Global contrast can better show a large saliency target, but the details of the edge of the image is not good enough, at the same time there are still many unrelated interference pixels in the background region. Therefore, the more popular global contrast and local contrast characteristics are used in the low-level feature of the image, considering the overall degree of difference and the edge and contour information of the object, the global contrast saliency map and the local contrast saliency map are obtained. Finally, a new fusion strategy with linear and nonlinear are adopted to different situations in the feature fusion, to obtain high quality saliency map. Result The method of saliency detection based on multiple priorities and comprehensive contrast are conducted on MSRA-1000 and DUT-OMRON benchmark datasets. Experimental results show that compared with 10 state-of-the-art methods, the proposed method reaches higher precision,recall,and F-measure, which compared with RBD algorithm are improved by more than 1.5% and the comprehensive performance is better than any of the compared methods. Conclusion In contrast to the method based on the low-level features and a single prior,the proposed method based on Multiple Priorities and Comprehensive Contrast can extract more minute features of the input image.The saliency maps not only show global contrast but also have highly detailed information.The proposed method can uniformly highlight the salient region and effectively suppress the complex background area. The result is more in line with visual perception.