test
server time: root: http://itiis.org
current_path: /journals/tiis/digital-library/21903
current_url: http://itiis.org/journals/tiis/digital-library/21903
Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds

Vol. 12, No. 10, October 30, 2018
10.3837/tiis.2018.10.019, Download Paper (Free):

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.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
S. Yang, F. Liu, J. Chen, D. Xiao and H. Zhu, "Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds," KSII Transactions on Internet and Information Systems, vol. 12, no. 10, pp. 4976-4994, 2018. DOI: 10.3837/tiis.2018.10.019.

[ACM Style]
Sai Yang, Fan Liu, Juan Chen, Dibo Xiao, and Hairong Zhu. 2018. Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds. KSII Transactions on Internet and Information Systems, 12, 10, (2018), 4976-4994. DOI: 10.3837/tiis.2018.10.019.