.png){kind=icon}
Return
Estimating Visual Saliency Through Single Image Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2013.2268868.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the regional saliency values on each specific image to simultaneously meet multiple saliency hypotheses on visual rarity, center-bias and mutual correlation. Experimental results show that our approach can outperform 14 state-of-the-art approaches on a public image benchmark.
Keywords:
Quadratic programming
single image optimization
visual saliency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSave
ShareSaveDodecaphenylporphyrin. Unusual optical properties of a novel sterically hindered hybrid porphyrin
PREAI
ShareSave
View more