.png){kind=icon}
.png){kind=icon}
Return
Multi-scale salient object detection using graph ranking and global-local saliency refinement
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2016.07.007.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local-global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Salient object detection (SOD)
Multi-layer graphs
Random forests
Region and boundary information
Feature relevance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
2.7
Papers:
2.8K
Citations:
4.2K