TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds |
| 영문제목(English Title) |
Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds |
| 저자(Author) |
Sai Yang
Fan Liu
Juan Chen
Dibo Xiao
Hairong Zhu
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| 원문수록처(Citation) |
VOL 12 NO. 10 PP. 4976 ~ 4994 (2018. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
objectness
background prior
Bayesian fusion
graph-based manifold ranking
salient object detection
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| 파일첨부 |
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