.png){kind=icon}
返回
Removing Label Ambiguity in Learning-Based Visual Saliency Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2011.2179665.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Visual saliency is a useful clue to depict visually important image/video contents in many multimedia applications. In visual saliency estimation, a feasible solution is to learn a feature-saliency mapping model from the user data obtained by manually labeling activities or eye-tracking devices. However, label ambiguities may also arise due to the inaccurate and inadequate user data. To process the noisy training data, we propose a multi-instance learning to rank approach for visual saliency estimation. In our approach, the correlations between various image patches are incorporated into an ordinal regression framework. By iteratively refining a ranking model and relabeling the image patches with respect to their mutual correlations, the label ambiguities can be effectively removed from the training data. Consequently, visual saliency can be effectively estimated by the ranking model, which can pop out real targets and suppress real distractors. Extensive experiments on two public image data sets show that our approach outperforms 11 state-of-the-art methods remarkably in visual saliency estimation.
Keyword:
Label ambiguity
learning to rank
multi-instance learning (MIL)
visual saliency
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
查看更多内容