关键词: 显著性图, 对象检测, 似物性, 凸包, 边缘密度

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