|
利用群体稀疏性的图像自动标注
Automatic Image Annotation Using Group Sparsity |
|
| |
| 课程网址: |
http://videolectures.net/cvpr2010_zhang_aiau/
|
| 主讲教师: |
Shaoting Zhang
|
| 开课单位: |
新泽西州立罗格斯大学
|
| 开课时间: |
2010-07-19
|
| 课程语种: |
英语
|
| 中文简介: |
自动为图像分配相关文本关键字是一个重要问题。在过去的十年里, 提出了许多算法, 并取得了良好的性能。努力的重点是关键字的模型表示, 但功能的属性还没有得到很好的研究。在大多数情况下, 会预先选择一组要素, 但重要的要素属性并不能很好地用于选择要素。本文介绍了一种基于正则化的特征选择算法, 以利用特征的稀疏性和聚类特性, 并将其纳入图像注释任务。提出了一种从关键字相似性和相关性反馈中迭代获取相似和不同对的新方法。因此, 在注释框架中对关键字相似性进行了建模。设计了大量的实验来比较图像注释任务中应用的特征、特征组合和基于正则化的特征选择方法之间的性能, 从而深入了解图像中要素的属性批注任务。实验结果表明, 基于群稀疏度的方法比其它方法更准确、更稳定。
|
| 课程简介: |
Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus keyword similarity is modeled in the annotation framework. Numerous experiments are designed to compare the performance between features, feature combinations and regularization based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group sparsity based method is more accurate and stable than others.
|
| 关 键 词: |
计算机科学; 计算机视觉; 图像与视频检索
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-05-22：王淑红(课程编辑志愿者)
|
| 阅读次数: |
137
|
|
图 书 馆