目的 显著性检测已成为图像处理过程中的一个重要步骤，已被应用到许多计算机视觉任务中。虽然显著性检测已被研究多年并取得了较大的进展，但仍存在一些不足，例如在复杂场景中的检测不准确或检测结果夹带着背景噪声等。因此，针对已有图像显著性检测方法存在的不能有效抑制背景区域，或不能清晰突显出完整的目标区域的缺点，提出一种结合背景先验和前景先验信息的图像显著性检测算法。方法 首先选取图像的边界超像素作为背景区域，从而根据每个区域与背景区域的差异度来建立背景先验显著图；然后通过计算特征点来构建一个能够粗略包围目标区域的凸包，并结合背景先验显著图来选取前景目标区域，从而根据每个区域与前景目标区域的相似度来生成前景先验显著图；最后融合这两个显著图并对其结果进一步优化得到更加平滑和准确的显著图。结果 利用本文算法对MSRA10K数据库内图像进行显著性检测，并与主流的算法进行对比。本文算法的检测效果更接近人工标注，而且精确率和效率都优于所对比的算法，其中平均精确率为87.9%，平均召回率为79.17%，F值为0.852 6，平均绝对误差（MAE）值为0.113，以及平均运行时间为0.723 s。结论 本文提出了一种结合两类先验信息的显著性检测算法，检测结果既能够有效地抑制背景区域，又能清晰地突显目标区域，从而提高了检测的准确性。

Objective Saliency detection aims to automatically identify and localize the important or attractive regions from an image.In the recent years,many researchers have given particular attention to saliency detection and took it as an important step in image processing.Saliency detection has been applied to many computer vision tasks and applications,such as image retrieval,object detection and recognition.Although saliency detection has been studied for many years,there are still certain shortcomings.For example,the detection in complex scenes is inaccurate or the results of the detection contain background noises.Considering that several existing methods of image saliency detection cannot suppress the background regions effectively,or cannot highlight the complete object regions clearly,a novel saliency detection method combining background priori and foreground priori information was proposed to further improve accuracy.The background prior is an assumption that the regions along the image boundaries are background regions,and the foreground prior is to calculate a convex hull to locate the foreground object regions.Method The region saliency of an image is defined as its similarity to the foreground in addition to being defined as its contrast to the background.Therefore,background and foreground can be extracted with prior,and all the regions of an image can be compared with these background and foreground to generate a saliency map.First,we selected the superpixels from image boundaries as the background regions to compute a background-based saliency map based on the dissimilarity between each region and the background regions.Second,we applied the convex hull from interest points to approximately locate the foreground object.Convex hull of original image and filtered image were calculated because there were not only salient regions inside the convex hull,but also the background regions,and the intersection regions of the two convex hull regions were obtained to remove background regions to some extent.Then,the intersection regions were combined with the background-based saliency map to select the foreground object regions,so foreground-based saliency map could be generated based on the similarity between each region and the foreground object regions.Finally,we integrated the two saliency maps utilize their respective advantages because the background-based saliency map could highlighted the object more uniformly and the foreground-based saliency map could better suppress the background noises.Then,the unified saliency maps was further refined to obtain a smoother and accurate saliency map.Result To test the performance of the proposed algorithm,experiments were conducted on the MSRA10K datasets,which contained 10 000 images and was one of the largest publicly available datasets.The results demonstrated that the saliency map of the proposed algorithm are closer to the ground truth and the proposed method performed favorably against the state-of-the-art methods in terms of accuracy and efficiency.The average precision,average recall,F-measure,MAE,and average running time of the proposed method are 87.9%,79.17%,0.852 6,0.113,and 0.723 s,respectively.Conclusion Saliency detection is a promising preprocessing operation in the field of image processing and analysis.We proposed a new method to detect saliency based on a combination of two kinds of prior information.The detection results of the proposed algorithm could not only effectively suppress the background noises,but also clearly highlight the object regions,thus improving the accuracy of the detection.