Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds
Sai Yanga b✉, Fan Liuc d, Juan Chena b e, Dibo Xiaoa b, Hairong Zhua b
- aSchool of Electrical Engineering , Nantong University
- bSchool of Electrical Engineering, Nantong University
- cCollege of Computer and Information , Hohai University Nanjing , Jiangsu- China
- dCollege of Computer and Information, Hohai University Nanjing, Jiangsu-China
- eNantong , Jiangsu - China
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
The diffusion-based salient object detection methods have shown excellent detection results and more efficient computation in recent years.However, the current diffusion-based salient object detection methods still have disadvantage of detecting the object appearing at the image boundaries and different scales.To address the above mentioned issues, this paper proposes a multi-scale diffusion-based salient object detection algorithm with background and objectness seeds.In specific, the image is firstly over-segmented at several scales.Secondly, the background and objectness saliency of each superpixel is then calculated and fused in each scale.Thirdly, manifold ranking method is chosen to propagate the Bayessian fusion of background and objectness saliency to the whole image.Finally, the pixel-level saliency map is constructed by weighted summation of saliency values under different scales.We evaluate our salient object detection algorithm with other 24 state-of-the-art methods on four public benchmark datasets, i.e., ASD, SED1, SED2 and SOD.The results show that the proposed method performs favorably against 24 state-of-the-art salient object detection approaches in term of popular measures of PR curve and F-measure.And the visual comparison results also show that our method highlights the salient objects more effectively.
TL;DRThe proposed multi-scale diffusion-based salient object detection algorithm with background and objectness seeds performs favorably against 24 state-of-the-art salient object Detection approaches in term of popular measures of PR curve and F-measure and visual comparison results show that the method highlights the salient objects more effectively. (Semantic Scholar)