图 书 馆
图像分割中的学习问题Learning issues in image segmentation |
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| 课程网址: | http://videolectures.net/lmcv04_buhmann_liis/ |
| 主讲教师: | Joachim M. Buhmann |
| 开课单位: | 苏黎世联邦理工学院 |
| 开课时间: | 2007-02-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Image segmentation is often defined as a partitioning of pixels or image blocks into homogeneous groups. These groups are characterized by a prototypical vector in feature space, e.g., the space of Gabor filter responses, by a prototypical histograms of features or by pairwise dissimilarities between image blocks. For all three data formats cost functions have been proposed to measure distortion and, thereby, to encode the quality of a partition. Learning in image segmentation can be defined as the inference of prototypical descriptors of segments like codebook vectors or average feature probability within a segment. Contrary to classification or regression, the empirical risk of image segmentation is often composed of sums of dependent random variables like in Normalized Cut, Pairwise Clustering or k-means clustering with smoothness constraints. One of the core challenges for machine learning is to discover what kind of information can be learned from these data sources assuming MRF cost functions as image models. The validation procedure for image segmentations strongly depends on this issue. I will demonstrate the learning and validation issue in the context of image analysis based on color and texture features. |
| 关 键 词: | 图像分割; 特征空间; 成对聚类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-11-29：chenjy |
| 最后编审: | 2022-11-29：chenjy |
| 阅读次数: | 18 |