Salient-region detection in a multi-level framework of image smoothing with over-segmentation
Hongyun Gao, Kin‐Man Lam · 2014
Saliency detection is one of the extraordinary abilities of the human visual system; it also provides a powerful tool for predicting where people tend to focus in the free-viewing process. In this paper, we propose a novel salient-object detection method which applies an over-segmentation-based saliency detection algorithm to multi-level smoothed images. The original image is initially subjected to smoothing based on multi-level L0gradient minimization; this can characterize its fundamental constituents while diminishing the insignificant details. Then, segment-based saliency computation is applied to the multi-level smoothed images to produce a series of intermediate saliency maps. The final saliency map is generated by combining the intermediate saliency maps. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of Precision, Recall and F-measure, as well as in terms of the area under the ROC curve (AUC).