| 图像依赖的显著图融合方法 |
| |
| 引用本文： | 梁晔,马楠,刘宏哲. 图像依赖的显著图融合方法[J]. 山东大学学报(工学版), 2021, 51(4): 1-7. DOI: 10.6040/j.issn.1672-3961.0.2020.266 |
| |
| 作者姓名： | 梁晔 马楠 刘宏哲 |
| |
| 作者单位： | 北京联合大学信息服务工程重点实验室,北京100101;北京联合大学机器人学院,北京100044;北京联合大学机器人学院,北京100044;北京联合大学信息服务工程重点实验室,北京100101 |
| |
| 基金项目： | 国家自然科学基金资助项目(61871038);国家自然科学基金资助项目(61871039);人才强校优选计划领军计划资助项目(BPHR2020AZ02);北京联合大学科研资助项目(ZK30202107) |
| |
| 摘 要： | 提出基于脊回归的显著图融合方法以获得更好的检测效果.在训练集中寻找待检测图像的近邻图像集,对近邻图像集采用脊回归方法对多种显著性检测方法的融合系数进行估计,进而对不同检测方法的显著图进行融合.该方法充分考虑了检测方法的差异性,很好的解决检测图像在没有基准二值标注下显著图的融合问题.试验采用流行的显著性数据集和显著性检测...
|
| 关 键 词： | 视觉注意力机制 显著性检测 显著性融合 图像依赖 脊回归 |
| 收稿时间： | 2020-03-31 |
Image-dependent fusion method for saliency maps |
| |
| Ye LIANG,Nan MA,Hongzhe LIU. Image-dependent fusion method for saliency maps[J]. Journal of Shandong University of Technology, 2021, 51(4): 1-7. DOI: 10.6040/j.issn.1672-3961.0.2020.266 |
| |
| Authors: | Ye LIANG Nan MA Hongzhe LIU |
| |
| Affiliation: | 1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China2. College of Robotics, Beijing Union University, Beijing 100044, China |
| |
| Abstract: | A saliency fusion method based on ridge regression was proposed to obtain better detection performance. The nearest neighbor set of the image to be detected was searched in the training set. The ridge regression method was used to estimate the fusion coefficients of different saliency maps. The saliency maps of different detection methods were fused. This method fully considered the differences of detection methods, and solved the problem of saliency map fusion in the absence of benchmark binary annotations. The AUC value of the proposed method was 0.911 on ECSSD dataset. The AUC value of the proposed method was 0.987 on HKU-IS dataset. The AUC value of the proposed method was 0.953 on DUT-OMRON dataset. The efficiency of the proposed method was verified by experimental results. |
| |
| Keywords: | visual attention mechanism saliency detection saliency fusion image-dependence ridge regression |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《山东大学学报(工学版)》浏览原始摘要信息 |
|
点击此处可从《山东大学学报(工学版)》下载全文 |
