目的 图像的显著性目标检测是计算机视觉领域的重要研究课题。针对现有显著性目标检测结果存在的纹理细节刻画不明显和边缘轮廓显示不完整的问题，提出一种融合多特征与先验信息的显著性目标检测方法，该方法能够高效而全面地获取图像中的显著性区域。方法 首先，提取图像感兴趣的点集，计算全局对比度图，利用贝叶斯方法融合凸包和全局对比度图获得对比度特征图。通过多尺度下的颜色直方图得到颜色空间图，根据信息熵定理计算最小信息熵，并将该尺度下的颜色空间图作为颜色特征图。通过反锐化掩模方法提高图像清晰度，利用局部二值算子（LBP）获得纹理特征图。然后，通过图形正则化（GR）和流行排序（MR）算法得到中心先验图和边缘先验图。最后，利用元胞自动机融合对比度特征图、颜色特征图、纹理特征图、中心先验图和边缘先验图获得初级显著图，再通过快速引导滤波器优化处理得到最终显著图。结果 在2个公开的数据集MSRA10K和ECSSD上验证本文算法并与12种具有开源代码的流行算法进行比较，实验结果表明，本文算法在准确率-召回率（PR）曲线、受试者工作特征（ROC）曲线、综合评价指标（F-measure）、平均绝对误差（MAE）和结构化度量指标（S-measure）等方面有显著提升，整体性能优于对比算法。结论 本文算法充分利用了图像的对比度特征、颜色特征、纹理特征，采用中心先验和边缘先验算法，在全面提取显著性区域的同时，能够较好地保留图像的纹理信息和细节信息，使得边缘轮廓更加完整，满足人眼的层次要求和细节要求，并具有一定的适用性。

Objective Saliency object detection has been widely used in many fields, such as image matching. Although the current saliency object detection algorithm has achieved good results, the following problems still exist:the texture detail is not obvious and the edge contours are incomplete. In addition, the saliency detection results of images are influenced by many factors, such as contrast and texture, and the reliability of the saliency detection results based on a single saliency factor is low. Hence, to solve these problems, a method of saliency object detection based on multiple features and prior information is proposed, this method can obtain final saliency images with prominent saliency areas, high brightness contrast, clear levels, distinct texture details, and complete edge contours. Method First, the convex hulls of the image are extracted, the points near the boundary in the convex hulls are removed, and the set of points of interest (i.e., the hull) is preserved. Meanwhile, the superpixel segmentation method is used to obtain compact image blocks with a uniform size, calculate the contrast and spatial distribution of each image block, the contrast and spatial distribution are fused linearly to obtain the global contrast map, calculate the prior probability and likelihood probability based on the hull and global contrast map. The Bayesian algorithm is utilized to obtain the contrast feature map. Under multi-scale conditions, the color histogram of the image is calculated and used to obtain the color spatial map. In accordance with information entropy theory, the information entropy of each color spatial map is calculated, the minimum information entropy is obtained, and the image is used with this scale as the color feature map. The unsharp mask method is adopted to improve the sharpness of the original image, enhance the edge of the image, and highlight other details. The local binary pattern operator is employed to obtain the texture feature map of the image, and the popular graph regularized and manifold ranking algorithms are used to obtain the center prior map and edge prior map. Finally, the primary saliency map is obtained by using the cellular automation fusion contrast feature map, color feature map, texture feature map, center prior map and edge prior maps. The primary saliency map is optimized with a fast guided filter to obtain the final saliency map. Result To confirm the availability and accuracy of the proposed algorithm, its performance is tested on two open datasets, namely, MSRA10K and ECSSD (extended complex scence saliency datay base). The MSRA10K dataset is one of the most frequently used datasets for comparing saliency test results. It contains 10 000 images and their corresponding ground truth images. The images in the datasets are surrounded by the bounding box of the artificial marker, and the background is simple. The ECSSD dataset contains 1 000 images and their corresponding ground truth images. The images in this dataset contain multiple targets, which are close to natural images and have an extremely complex background. Under the same experiment environment, 200 images are randomly selected from each dataset and compared with 12 saliency object detection methods with an open-source code based on multi-information fusion. Experimental results show that the proposed saliency object detection method based on multiple features and prior information is significantly improved in terms of PR (precision-recall) curves, ROC (receiver operating characteristic) curves, F-measure, MAE (mean absolute error), and S-measure. Its overall performance is better than that of the compared algorithms, and it can solve the above mentioned problems well. On the MSRA10K and ECSSD datasets, the PR curves of the proposed algorithm are the closest to the upper right, and the DCL (diffusion-based compactness and local wntrast) algorithm is close to our algorithm, both of which are higher than the other compared algorithms. The ROC curves of the BSCA (background-based map optimized via single-lager cellular automata) and DCL algorithms are closer to the upper left than our algorithm on the MSRA10K dataset. Our algorithm is close to the ROC curves of the DCL algorithm, and they are better than the other compared methods on the ECSSD dataset. The F-measure values of our algorithm are the highest and reach 0.944 49 and 0.855 73. The values for the popular SACS (self-adaptively weighted co-saliency detection via rank constraint), BSCA, DCL, and WMR (weighted manifold ranking) algorithms are slightly lower than that of the proposed algorithm, which indicates that our algorithm has an optimal overall performance. The MAE values of our algorithm are the smallest and reach 0.070 8 and 0.125 71, indicating that our algorithm has the best detection effect. The S-measure values of our algorithm are the highest and reach 0.913 26 and 0.818 88, indicating that the salient image of our algorithm is the more similar to the structure of the ground truth image, and the detection effect is perfect. Conclusion In this study, a saliency object detection method based on multiple features and prior information is proposed. This method fully combines the advantages of contrast features, color features, texture features, center prior information, and edge prior information. It comprehensively extracts the salient region and preserves the texture and detail information of the image well. Thus, the edge contour is more complete. The proposed method also satisfies the hierarchical and detail requirements of the human eye and has a certain applicability. However, the method is not perfect when dealing with the no-salient region of complex images. Optimization will be considered in future research.