Abstract: A novel salient object detection approach was proposed based on visual saliency map and objectness for detecting salient objects in images. For each input image, a number of bounding boxes with high objectness scores were exploited to estimate the rough object location, and a scheme of transferring the bounding box-level objectness score to pixel level was used to weight the input saliency map. The input saliency map and the weighted saliency map were adaptively binarized and the convex hull algorithm was used to obtain the maximum search region and the seed region, respectively. Finally, a global optimal solution was obtained by combining the edge density with the search region and seed region. The experimental results on the public MSRA-B dataset with 5000 images show that the proposed approach outperforms the maximum saliency region method, the region diversity maximization method and the objectness detection method in terms of precision, recall and F-measure.

Key words: saliency map, object detection, objectness, convex hull, edge density

摘要： 为了能够准确地检测出图像中的显著性对象,提出了一种新的基于视觉显著性图与似物性的对象检测算法。该算法首先在图像上提取大量具有较高似物性度量的矩形窗口,并估算出对象可能出现的位置,将窗口级的似物性度量转换到像素级的似物性度量;然后把原始显著性图与像素级的似物性图进行融合,生成加权显著性图,分别二值化原始显著性图和加权显著性图,利用凸包检测得到最大查找窗口区域与种子窗口区域;最后结合边缘概率密度搜索出最优的对象窗口。在公开数据集MSRA-B上的实验结果表明,该算法在准确率、召回率以及F-测度方面优于最大化显著区域检测算法、区域密度最大化算法以及似物性对象检测算法等已有的多种算法。

关键词: 显著性图, 对象检测, 似物性, 凸包, 边缘密度