图 书 馆
计算机视觉鲁棒分类器的设计On the design of robust classifiers for computer vision |
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| 课程网址: | http://videolectures.net/cvpr2010_masnadi_shirazi_drc/ |
| 主讲教师: | Hamed Masnadi-Shirazi |
| 开课单位: | 加州大学 |
| 开课时间: | 2010-07-13 |
| 课程语种: | 英语 |
| 中文简介: |  研究了鲁棒分类器的设计，该分类器可以与计算机视觉中典型的嘈杂和离群值较高的数据集抗衡。有人认为，这种鲁棒性要求损失函数同时惩罚较大的正和负边距。采用分类器设计的概率启发视图，确定了设计此类损失的必要条件。这些条件用于推导新颖的鲁棒贝叶斯一致性损耗（表示为Tangent损耗）和关联的增强算法（表示为TangentBoost）。对来自场景分类，对象跟踪和多实例学习的计算机视觉问题的数据进行的实验表明，TangentBoost始终优于以前的增强算法。 |
| 课程简介: | The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires loss functions that penalize both large positive and negative margins. The probability elicitation view of classifier design is adopted, and a set of necessary conditions for the design of such losses is identified. These conditions are used to derive a novel robust Bayes-consistent loss, denoted Tangent loss, and an associated boosting algorithm, denoted TangentBoost. Experiments with data from the computer vision problems of scene classification, object tracking, and multiple instance learning show that TangentBoost consistently outperforms previous boosting algorithms. |
| 关 键 词: | 增强算法; 贝叶斯 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-25：zyk |
| 最后编审: | 2021-03-25：zyk |
| 阅读次数: | 51 |