|
利用小波特征和空间原型标记图像区域
Labelling Image Regions Using Wavelet Features and Spatial Prototypes |
|
| |
| 课程网址: |
http://videolectures.net/samt08_saathoff_liruf/
|
| 主讲教师: |
Carsten Saathoff
|
| 开课单位: |
科布伦茨 - 兰道大学
|
| 开课时间: |
2008-12-18
|
| 课程语种: |
英语
|
| 中文简介: |
在本文中，我们提出了一种图像区域分类方法，它将低级处理与高级场景理解相结合。对于低级训练，使用直接从图像像素提取的小波特征对预定义图像概念进行统计建模。对于分类，与这些统计模型相比较的给定测试区域的特征提供了对所有可能的图像概念的概率评估。最大化这些值本身已经导致分类结果（标签）。然而，在我们的论文中，它们被用作利用明确表示的标签空间排列的高级方法的输入，所谓的空间原型。我们使用模糊约束满足问题和线性规划来形式化问题。它们提供了一个具有明确知识的模型，适用于区域标记的任务。对超过6000个测试图像区域进行的实验结果表明，使用低水平和高水平图像分析的组合显着提高了标记准确性。
|
| 课程简介: |
In this paper we present an approach for image region classification that combines low-level processing with high-level scene understanding. For the low-level training, predefined image concepts are statistically modeled using wavelet features extracted directly from image pixels. For classification, features of a given test region compared with these statistical models provide probabilistic evaluations for all possible image concepts. Maximizing these values themselves already leads to a classification result (label). However, in our paper they are used as an input for the high-level approach exploiting explicitly represented spatial arrangements of labels, so called spatial prototypes. We formalize the problem using Fuzzy Constraint Satisfaction Problems and Linear Programming. They provide a model with explicit knowledge that is suitable to aid the task of region labeling. Results of experiments performed for more than 6000 test image regions show that using the combination of low-level and high-level image analysis increases the labeling accuracy significantly.
|
| 关 键 词: |
图像区域分类; 小波特征; 统计模型
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2019-09-17：lxf
|
| 阅读次数: |
49
|
|
图 书 馆